An Iterative Regression Model For Predicting The Likelihood of Accident Occurrence in Roads Network

Abstract

Today, there is an earnest need for a communication network in road system management to improve traffic control and road management to solve the increasing problem of urban traffic accidents. Road accidents have been a concern worldwide; there is a prediction that there will be an astronomical increase in incidents by 2030. Researchers have attempted to predict and stress that the compulsion of legislation and promotion of road safety awareness help reduce road gridlock accidents. The objectives are to present an iterative regression model for foretelling road traffic accidents and assess the confusion matrix to model the performance using accuracy, precision, and recall metrics. The model developed increases the forecast accuracy of road gridlock accidents compared to the existing model and classifies binary datasets. The model also helps people to better understand road networks and the results of floating traffics on roads.