SMART CITY TRANSPORTATION: DEEP LEARNING ENSEMBLE APPROACH FOR TRAFFIC ACCIDENT DETECTION

Abstract

Rapid urbanization and the increasing number of vehicles on road networks have significantly contributed to the rise in traffic accidents, posing serious challenges to public safety and transportation management. Smart city initiatives aim to leverage advanced technologies to improve traffic monitoring, reduce accidents, and enhance transportation efficiency. This paper presents a deep learning ensemble approach for traffic accident detection in smart city transportation systems. The proposed framework utilizes traffic surveillance videos and image data collected from intelligent transportation infrastructure to automatically detect traffic accidents in real time. Multiple deep learning models are integrated within an ensemble architecture to improve detection accuracy, robustness, and reliability. The system performs image preprocessing, feature extraction, object detection, and accident classification to identify collision events and abnormal traffic situations. By combining the strengths of different deep learning models, the ensemble approach effectively handles complex traffic environments, varying weather conditions, and diverse accident scenarios. Experimental analysis demonstrates that the proposed framework achieves high detection accuracy, reduces false alarms, and supports timely emergency response. The developed system provides an intelligent and scalable solution for enhancing road safety and traffic management in smart cities.