Spatiotemporal interactive dynamic adaptive adversarial graph convolution network for traffic flow forecasting

Accurate traffic flow forecasting is important for intelligent traffic management and control. To address the inability of existing methods to simultaneously capture the spatiotemporal dependence of traffic flows and the significant trend differences between predicted and real values, a traffic flow forecasting method based on a Spatiotemporal Interactive Dynamic Adaptive Convolutional Network (STIDAAG) is proposed. First, an interactive learning structure is designed to dynamically aggregate the spatiotemporal characteristics of the hidden nodes in the traffic network. Second, a dynamic adaptive graph generation network is designed based on the current and historical state to further capture the dynamic spatiotemporal characteristics. Finally, the adversarial graph convolutional network is used to optimize the loss for adversarial training to reduce the trend difference between the predicted and true values. The results of the experiment on four publicly available datasets indicate that STIDAAG outperforms both typical and advanced methods in terms of predictive performance.