Traffic Accident Analysis using Random Forest and Bagged Tree Classifier

Foreseeing the danger of an accident, mishap monitoring does just that. Reckless driving, poor road design, and the presence of socially sensitive elements are all significant contributors to traffic accidents. Tracking traffic helps gather data for modeling and testing detection systems. Investigative analysis of past incidents, vehicle movement, transportation systems, and other variables is often used to identify this tendency. Besides natural causes of morbidity and mortality, wounds inflicted in car crashes are a major contributor to mortality rates. Because of this, it’s important to examine what causes car crashes and how such incidents might be avoided. In order to determine how to best categorize road accidents into mild, moderate, and catastrophic categories, researchers ran a dataset of events through a battery of Machine Learning (ML) classification algorithms. Foretelling crucial characteristics requires feature selection, and the Random Forest Algorithm excels at this. The severity of disasters may be categorized with the help of Bagged Tree Classifier.