Alanoud Almaazmi
ABSTRACT
The severity of roadway traffic crashes is a significant concern for highway safety officials around the world. Studying crash severity levels and contributing risk factors could lead to considerable savings in human life and economic losses. The study's main objective is to utilize a well-known machine learning technique, namely Random Forest, to analyze the severity levels of Sharjah's roadway traffic crashes. The study will identify the most critical exploratory factors among 23 roadway, driver, and environmental factors contributing to the crash's severity in terms of injury level. Towards this, a crash dataset for the 2015-2019 period was obtained from Sharjah Police, which included a total of 2,538 crash records, divided into three crash severity levels: fatal, hospitalized injury, and not-hospitalized injury. Several performance measures were computed using the h2o machine learning tool to find out the best-performing model, including confusion matrix, precision, recall, F1-measurement, among others. The dataset was split into a 70% training set and a 30% testing set. While the training set was used to develop the various models, the testing set was used to validate each model. Finally, the developed models were optimized by fine-tuning their parameters. The best-performing RF models achieved a classification accuracy for the training and test dataset is 61.9% and 58.2%, respectively, which is acceptable for classifying crash severity levels. The results revealed that the most significant factors associated with crash severity level in Sharjah were month of the year, collision type, day of the week, vehicle occupant, and nationality.
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Index Terms
- Severity Analysis of Sharjah Crashes Using Random Forest
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