A review on neural network techniques for the prediction of road traffic accident severity

Abstract

Abstract The occurrence rate of death and injury due to road traffic accidents is rising increasingly globally day by day. For several decades, the focus of research has been on getting a deeper understanding of the significant factors that influence the risk of road traffic fatalities. In today's modern world, neural network (NN) approaches play a crucial role in identifying the contributing factors that describe the frequency and severity of road accidents. Over the years, many researchers used neural network models for predicting the impact of such factors on road accident injury severity. Deep learning methods such as the recurrent neural network (RNN) and the convolutional neural network (CNN) has recently been successfully used for the prediction of road accidents and demonstrate their high accuracy and efficiency. This study overview and summarizes the different forms of neural network models such as the single layer perceptron (SLP) neural network, the multilayer layer perceptron (MLP) neural network, the radial basis function (RBF) neural network, the recurrent neural network, and the convolutional neural network used as a prediction method for the severity of road crash injuries and includes a discussion of future planning and difficulties. This article also summarizes the model input parameter or independent variable and output or dependent variable, as well as various performance assessment methods.