A Resource-Aware Multi-Graph Neural Network for Urban Traffic Flow Prediction in Multi-Access Edge Computing Systems

Predicting traffic is the main duty of an intelligent transportation system (ITS). Precise traffic forecasts can significantly enhance the use of public funds. However, the dynamic and complex nature of spatio-temporal relationships presents significant challenges. Most current methods utilize static adjacency matrices, leading to reduced forecasting accuracy and precision. This approach fails to account for the complex spatio-temporal correlations that interact simultaneously. In order to show how different spatio-temporal correlations change over time in the traffic flow network, this study suggests a unified simultaneous Multi Fusion Graph Network (DMFGNet) model. The goal of the suggested DMFGNet model is to identify dynamic spatio-temporal linkages between various regions. Meanwhile, we propose a model, the Spatio-Temporal Attention Unit (STAU), to control the weights of neighbor aggregation. It is capable of meticulously combining spatio-temporal characteristics from different neighbors. We tested the model for both real-time and pre-processed predictions using a combination of edge and cloud infrastructure. This setup performs prediction tasks at the edge layer and conducts training in the remote cloud. This approach guarantees the use of only relevant data for model training and prediction-making, thereby boosting the system's overall effectiveness. This approach not only optimizes resource allocation but also aids in reducing latency and enhancing the overall performance of cloud-based prediction models, potentially enhancing the capabilities of consumer technology and electronics solutions. We carefully tested and evaluated two large real-world traffic flow datasets to show that the proposed method works and is useful. The results of the tests show that the suggested model is better than the current best baseline methods. Additionally, the results demonstrate the effectiveness and usefulness of the recommended strategy.