Application of Attention Fusion Dynamic Graph Convolutional Networks Enhanced with Hub Nodes in Traffic Flow Forecasting

Real-time and precise traffic flow prediction serves as a vital enabler for modern intelligent transportation systems. In this domain, graph neural networks demonstrated remarkable proficiency in capturing the intricate spatiotemporal relationships embedded within traffic data. However, we identify a critical limitation in existing attention-based dynamic GNNs: their inadequate modeling of hub nodes, which maintain extensive connections and exhibit more intricate spatiotemporal patterns than ordinary nodes in transportation networks.To address this gap, we propose the Hub Node-enhanced Attention Fusion Dynamic Graph Convolutional Network (HN-AT-DGCN), a novel encoder-decoder framework comprising four essential components. The multi-scale feature fusion module first extracts traffic flow characteristics across varied temporal scales. The encoder then employs a dynamic graph convolutional gated recurrent unit to capture comprehensive spatiotemporal dependencies. A multi-head temporal attention mechanism further models long-range temporal patterns, while a 2D graph convolutional decoder ultimately generates future traffic flow predictions.Our key contributions are twofold. We introduce a hub node identification module that automatically detects critical nodes in the network. Our methodology incorporates a novel dynamic graph convolution scheme that facilitates discriminative feature learning across three semantic levels: node-wise properties, structural correlations, and hub-dominated propagations, comprehensively modeling multi-faceted relationships in traffic networks.The effectiveness of our method is evidenced by thorough evaluations across three authentic traffic datasets, where it attains leading performance and exceeds 15 baseline approaches.