Analysis of Traffic Accident Features and Crash Severity Prediction

Abstract

Vehicle crashes occur because of numerous factors. They lead to loss of lives and permanent incapacity. The budgetary expenses of both individuals as well as the nation are influenced by vehicle crashes. According to road accident statistics, a total of 464,910 road accidents were reported in India, claiming 147,913 lives and causing injuries to 470,975 persons every year. In this work, the UK data set sourced from Kaggle is used. For the study, 17 attributes and 35 thousand records of the year 2015 are considered. The data set is imbalanced, so to balance out the data, the over-sampling technique is used. Random forest, decision tree, logistic regression, and gradient naïve Bayes algorithms are used to predict the severity of accidents. To evaluate the model, performance measures like accuracy, precision, recall, F1-score are used. When accuracy, precision, F1-score performance measure are considered, random forest yielded the best result. When recall performance measure is used, random forest for fatal, decision trees for serious, logistic regression for slight yielded the best result.