A Comparative Study of Hybridized Neural Networks in Estimating Traffic Accident Severity

Abstract

The increasing number of populations causing increase of vehicles which leads to traffic accident. As transportation system expands, it needs to be monitored to assure safety to citizen. Cities are trying to adopt technological advancement in order to minimize traffic accident. Traffic accidents have become one of the largest national health issues and many factors like weather condition, road condition, light condition, etcetera is related to it. In the current paper, several hybridize machine learning models are used on dataset of city Leeds, UK to estimate traffic accident severity. Hybridize Machine learning models are Artificial Neural Network (ANN) with Gradient decent, Principle Component Analysis (PCA) with ANN, Genetic Algorithm with ANN, Particle Swarm Optimization with ANN. These models are also compared with other machine learning models such as Support Vector Machine (SVM), Naïve Bayes, Nearest Centroid, Logistic Regression, K Nearest Neighbor Classification and Random Forest. Comparison was done considering performance evaluation of each model’s accuracy result. Genetic Algorithm with ANN showed promising result of 86.63% accuracy which is the highest score of all model results. Whereas, Nearest Centroid Method gave 55% of accuracy resulting lowest of all. The Results and findings obtained in this study are significant which can provide invaluable information on reducing traffic accident.