Intelligent Traffic Identification System Powered byConvolutional Neural Networks

Abstract: In model cities, managing traffic flow is paramount, particularly during peak hours and festive periods. Accurate and real-time traffic information is crucial for effective urban planning and transportation management. In response to this challenge, our research introduces a novel solution based on Convolutional Neural Networks (CNNs) to identify traffic flow patterns in urban environments using 2D traffic images as the primary dataset. This dataset comprises two categories: "High Traffic" and "Normal Traffic," which are strategically partitioned into a 70:30 training and testing ratio to facilitate the training of our proposed CNN models. The performance of various CNN models is assessed through a rigorous evaluation process, and the best-performing model is identified based on evaluation metrics, including accuracy, precision, recall, F1-Score, and ROC values computed from confusion matrices. Our results showcase the feasibility of CNNs in addressing the traffic flow identification problem, providing valuable insights for urban planners, traffic engineers, and local authorities. The proposed Convolutional Neural network with three layers using the cropped dataset of images (CNN3*) based solution has the potential to significantly enhance traffic management efficiency and alleviate congestion in model cities, ultimately contributing to improved urban liveability and mobility. This research underscores the importance of leveraging deep learning techniques to tackle complex urban challenges and paves the way for future advancements in traffic flow analysis and smart city development.