Road Accident Severity Prediction Using Random Forest Algorithm

Abstract

Abstract: The surge in accident rates, intensifying human casualties, has propelled the integration of cameras and fixed speed cameras into daily activities. This study focuses on predicting road accident severity, a crucial advancement in road accident management, especially for urban emergency logistics. Employing Machine learning methodologies such as Random Forest which is commonly used for predictive analysis, Naive Bayes, and logistic regression, we rigorously evaluate their efficacy in densely populated areas. The research implements and compares these algorithms, utilizing a confusion matrix to illustrate interclass impacts on pedestrians, vehicle or pillion passengers, and drivers or riders. Notably, the severity prediction for road accidents achieves an impressive 86.8% accuracy with Random Forest, surpassing SVM's 82%. This exemplifies the effectiveness of machine learning in enhancing accuracy and reliability, providing valuable insights for proactive road safety measures.