Cloud Edge Hybrid Deep Learning Framework for Real Time Traffic Management

Abstract

Traffic in the city is a challenge that has serious repercussions as far as loss of money, environmental depreciation, and accidents are concerned. This paper proposes an end-to-end cloud and edge computing hybrid deep learning architecture suitable in managing traffic in intelligent transportation systems in real-time in order to counter these problems. The given framework will find the synergy of edge computing and centralized cloud resources in a bid to learn model optimization in a low-latency manner, maximizing scalability. On the edge, object detection models (like YOLOv8) and lightweight convolutional neural networks (CNNs) are implemented on embedded devices to allow in real-time analysis of a video that may be used to detect a vehicle, estimate traffic levels, or monitor incidents in intersections. In the meantime, the layer of the clouds is used to train big models and coordinate at the world level utilizing the information of historical traffic regimens using spatio-temporal deep learning models, such as Graph Convolutional Networks (GCN), Long Short-Term Memory (LSTM) networks, or Transformer-based architectures to predict the future of the traffic flows and inform the strategy. The effective task offloading, the synchronization of data, and the update of the model periodically are provided due to a dynamic communication mechanism between the edge and cloud nodes. The framework also reuses model compression methods to allow the compatibility of edge devices without sacrificing prediction performance. The cameras, GPS, and roadside units are fused with multi-modal sensor fusion which makes the data in use robust and decisions reliable. Real-world experiments on METR-LA, and CityFlow indicate that the proposed hybrid system can generate a 26% incident response time advancement, over 65% bandwidth utilization decrease because of edge preprocessing, and prediction surpassing the conventional constituent server-only or exclusively edge solutions. In addition to that, the architecture can consider flexibility to changing traffic demands and scalability to multi junction implementations. The article highlights the exciting possibilities of distributed intelligence as an approach to current mobility systems in urban setting and paves the way to integrating distributed intelligence with autonomous vehicles and vehicle-to-everything (V2X) technologies in the future, conferring on the former its suitability as one of the solutions employed in the second generation of smart cities.