The Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection

With rapid urbanization and increasing vehicular density, traffic accidents pose significant challenges to smart city transportation systems. Traditional accident detection methods rely on manual reporting and surveillancebased monitoring, which often lead to delays in emergency response and traffic management. This paper presents a deep learning ensemble-based approach for real-time traffic accident detection. The proposed system integrates multiple deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformer-based architectures, to analyze traffic data from diverse sources such as CCTV footage, IoT sensors, and GPS data. By leveraging ensemble learning techniques, the model improves detection accuracy, reduces false positives, and enhances real-time accident prediction. Experimental results demonstrate the effectiveness of the proposed approach in detecting accidents with high precision, ultimately contributing to safer and more efficient smart city transportation systems.