Leveraging IoT‐Aided Robotics for Enhanced Efficiency in Urban Traffic Management Utilizing Multi‐Component Attention Graph Convolutional Neural Network Intended for Real‐Time Traffic Monitoring Systems

ABSTRACT Maintaining traffic restrictions has become more difficult because growing number of vehicles on the road, particularly in locations with high populations. Efficient traffic monitoring and road safety management have become critical challenges due to the rapid increase in vehicular density in urban environments. This paper proposes a novel IoT‐aided robotic framework, termed MAGCNN‐RTMS‐IoT‐AR, for real‐time traffic sign detection and recognition using the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Initially, the input images are pre‐processed using an Innovation Saturated Koopman Kalman Filter (ISKKF) to enhance data quality and robustness against noise. Subsequently, a Multi‐component Attention Graph Convolutional Neural Network (MAGCNN) is employed to effectively capture spatial and contextual dependencies for accurate classification of traffic signs. To further improve model performance, a Gradient Jellyfish Search Algorithm (GJSA) is integrated for optimal parameter tuning. The proposed approach is evaluated using multiple performance metrics, including accuracy, precision, recall, F 1‐score, error rate, loss, and mean square error (MSE). Experimental results demonstrate that the proposed method; significantly outperforms existing models such as RTMS‐IoT‐LeNet‐5, EATS‐CR‐CNN, and ETSR‐DS‐VGG‐16, achieving notable improvements in 19.36%, 24.82%, and 31.36%, higher accuracy; 21.62%, 24.54%, and 30.54% higher precision. The integration of IoT, robotics, and optimized deep learning makes the proposed system highly suitable for real‐time intelligent transportation applications.