Paper Contents
Abstract
The occurrence of road accidents remains a prominent cause of deaths, disabilities, and hospitalizations in our country this makes accident mitigation important in order to minimise it and save lives. Accidents pose a significant threat to public safety, resulting in loss of life, injuries, and property damage. The urgent issue of road accidents by proposing a predictive model for accident severity. Traditional mitigation methods often lack real-time hazard identification, necessitating advanced solutions. Leveraging machine learning and data from the Road Traffic Accident (RTA) dataset, the proposed model employs the Random Forest algorithm to achieve an 83% accuracy rate in predicting accident severity. Factors such as vehicle type, age, sex, time of day, and weather conditions are analyzed to provide accurate predictions. The web application developed as part of this initiative allows users to input data, enabling personalized severity predictions. Ultimately, this research aims to significantly enhance road safety, reduce accidents, and safeguard lives and public wellbeing.
Copyright
Copyright © 2024 Akhil Reddy. This is an open access article distributed under the Creative Commons Attribution License.
