Signal Decoupling Optimization for Robust Graph-Based Traffic Forecasting

This article proposes a robust decoupling network named RDNet to provide stable traffic predictions even when perturbations exist in historical data. A decoupling block is designed in the RDNet for dividing traffic data into the invariable component (IC) and variable component (VC). The IC of historical or future data is estimated through the invariable block without historical data and thus would not be perturbed. The variable block is developed to forecast the VC of future data using the VC of historical data. Besides, the robust graph neural network and smoothing loss are designed to reduce the effects of perturbations. The RDNet fuses the obtained IC and VC of future data to produce the predictions, and the invariable and decoupling losses are developed for stabilizing the prediction. The results on six open datasets have demonstrated that the RDNet can achieve a 15.62% average improvement in accuracy compared with the state-of-the-art predictor.