A DEEP LEARNING ENSEMBLE APPROACH FOR THE DETECTION OF TRAFFIC ACCIDENTS IN SMART CITY TRANSPORTATION

Abstract

The dynamic and unpredictable nature of road traffic makes it necessary for smart cities to use efficient accident detection technologies in order to improve safety and streamline traffic management. This article presents a comprehensive exploration study of the various accident detection techniques that are currently in use. It sheds light on the nuances of other state-of-the-art methodologies while also providing a detailed overview of various types of traffic accidents, such as rear-end collisions, T-bone collisions, and frontal impact accidents.