Traffic Flow Prediction Model Based on Spectral Hypergraph Convolutional Network

The traditional graph structure ignores the implicit complex relationship between nodes to a certain extent. Aiming at the problem, hypergraph was used to represent traffic flow data at a high level, and a traffic flow prediction method was proposed based on hypergraph convolutional network in spectral domain. First, spectral domain hypergraph convolution and gated temporal convolution were used to extract the spatiotemporal characteristics of traffic flow at multiple scales by describing the relationship at the data feature level through dynamic hyperedges, and end-to-end node-level traffic prediction was realized. Afterward, the real historical data sets of Beijing and California were used to conduct prediction experiments. The ablation experiments verify the effectiveness of the proposed method by isolating and reconstructing the network model; the full-time and morning peak traffic flow prediction experiments show that the prediction accuracy of the proposed method is higher than that of the current mainstream traffic forecasting models.