Prediction of Risk Factors Influencing Severity Level of Traffic Accidents Using Artificial Intelligence

Abstract

Road safety is of major interest for highway and traffic engineers worldwide. In Jordan, road networks have recently displayed relatively high traffic volumes, specifically in urban centers and in the Central Business District (CBD) areas of major cities. Irbid is one of the major cities suffering from serious traffic accidents problems that should have received more attention from decision makers. Based on data of 94,356 accidents occurred over a five-year period (from 2013 to 2017), Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques have been employed to predict and classify traffic accidents in this city. ANN has been used to model the relationship between driver injury severity and traffic accident factors, such as age and gender of drivers, type and faults of vehicles, weather conditions and reasons of accidents. The structure of the used ANN model has been one input layer of 6 neurons, with one hidden layer of 15 neurons and one output layer of 3 neurons representing severity level. The ANN model has showed high correlation based on the high value of correlation coefficient R=0.87. The used ANN model has exhibited better results in predicting new accidents with MSE equal to 0.05, compared with SVM model with MSE of 0.09. Sensitivity analysis has been carried out on the trained neural network to identify the importance of crash-related factors. The traffic accident data have been used to build the classifier, using SVM. The overall model classification performance has been 90.4%, which accounts for the circumstances under which drivers are more likely to be killed or injured in a vehicle accident. It has been concluded that the comprehensive performance of the SVM model is better than the ANN model for traffic accidents classification.
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