Intelligent Model for Avoiding Road Accidents Using Artificial Neural Network

Abstract

Accidents typically occurred on roads, resulting in significant societal losses. Road accidents are a worldwide issue that result in the loss of precious human lives and property. The purpose of this paper is to create an intelligent system-based on Machine Learning model for avoiding road accidents, as well as a system that effectively reduces road accidents severities. The Artificial Neural Network (ANN) algorithm, along with others such as Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Nave Bayes (NB), Stochastic Gradient Descent (SGD), Random Forest (RF), Gradient Boosting (GB), and AdaBoost, is used to create an intelligence system. Many driving collaborator procedures, installed in a few vehicles, assist drivers in avoiding vehicle crashes by providing early cautioning messages. The intelligence road crash avoidance system model is built on dataset of 29 columns and 1048575 rows. Preprocessing, feature selection, and feature extraction performed with the help of heat map and correlation matrix are used to select features. Linear Discriminant Analysis (LDA) is used for feature extraction. The testing dataset revealed that the proposed ANN method outperforms other algorithms with an accuracy of 0.856. Intelligent systems aid in the prevention of traffic accidents, which aids police officers and researchers in developing new policies.