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…

• NN
  National Natural Science Foundation of China

  Award: 62373255
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2024A1515011204