Development of Prediction Models for Vulnerable Road User Accident Severity

Abstract

Road traffic accidents are considered a significant problem which ruins the life of many people and also causes major economic losses. So, this issue is considered a hot research topic, and many researchers all over the world are focusing on developing a solution to this most challenging problem. Traditionally the accident spots are detected by means of transportation experts, and following that, some of the statistical models such as linear and nonlinear regression were used for accident severity prediction. However, these traditional approaches do not have the capability to analyze the relationship between the influential factor and accident severity. To address this issue, an Artificial Neural Network (ANN) classifier based vulnerable accident prediction model is proposed in this current research. Initially, the past accident data over the past period of years is collected from a specified area. The acquired data consists of a variable factor related to road infrastructure, weather condition, area of the accident, type of injury and driving characteristics. Then, to standardize the raw input data, min-max normalization is used as a pre-processing technique. The pre-processed is sent for the feature selection process in which essential features are selected by correlating the variable factor with accident severity prediction. Following that, the dimension of the features is reduced using Latent Sematic Index (LSI). Finally, the reduced features are fetched into the ANN classifier for predicting the severity of accidents such as low, medium and high. Simulation analysis of the proposed accident prediction model is carried out by evaluating some of the performance metrics for three datasets. Accuracy, error, specificity, recall and precision attained for the proposed model using dataset 1 is 96.3, 0.03, 98 and 98%. Through this proposed vulnerable accident prediction model, the severity of accidents can be analyzed effectively, and road safety levels can be improved.