Accident Detection in Smart Cities: A Deep Learning Ensemble Approach

As smart city infrastructures have grown in popularity; traffic safety has emerged as a top technological objective. In order to increase the safety of urban transportation, this research proposes an ensemble deep learning method for real-time traffic accident identification. Our method uses video and sensor data to improve accident detection accuracy by merging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and decision trees. Our experimental findings on a sizable dataset show notable advancements over conventional techniques, attaining high recall and precision rates. This study demonstrates how AI-powered technologies can be used in smart cities to enhance public safety and proactively control traffic.