Intelligent Traffic Accident Detection Using a Deep Learning Ensemble in Smart City Transportation Systems

Abstract

 Traffic accidents pose significant challenges to urban mobility, safety, and smart city management. Traditional accident detection methods often rely on manual reporting or isolated sensor systems, resulting in delays that can increase congestion and reduce emergency response efficiency. This study proposes an Intelligent Traffic Accident Detection System using a Deep Learning Ensemble Framework designed specifically for smart city transportation environments. The system integrates multiple deep learning models—such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gradient Boosting Networks—to enhance accuracy, robustness, and real-time detection capability. Traffic video streams, sensor data, and vehicular telemetry are analyzed collaboratively by the ensemble to detect anomalies indicative of potential accidents. Experimental results demonstrate that the ensemble approach significantly outperforms individual models in terms of precision, recall, and detection speed. The proposed system not only supports proactive traffic management but also improves emergency response efforts, contributing to safer and more efficient smart city transportation networks.