Analysis of machine learning models for traffic accidents severity classification

Abstract

In the modern world, traffic accidents frequently result in fatalities and serious injuries. The ability of machine learning to foretell the severity of road traffic accidents has shown great promise. The classification of traffic accidents has shown to be a good application for algorithms like random forest. In this paper, performance on a specific dataset has been evaluated using random forest and other models. The dataset used for the analysis came from a publicly accessible source and contained information on several variables like the type of road, the time of day, and the weather. In order to analyze the severity of accidents, a number of algorithms were applied to the dataset, including decision tree, random forest classifier, and logistic regression algorithm. Each model was evaluated on parameters such as model accuracy, precision and recall of the model, and F1 score. The random forest classifier outperformed the other models, achieving an accuracy of 98.48%. The study concludes that machine learning algorithms can accurately predict the severity of road traffic accidents, which could help to reduce the number of accidents and fatalities on the road.