关键词: 交通流量预测, 图卷积循环网络, 元节点, 超网络, 注意力机制

Abstract: As a pivotal technique in traffic flow forecasting, Graph Convolutional Recurrent Networks (GCRN) effectively enhance prediction performance. However, given the pronounced spatio-temporal heterogeneity inherent in traffic data, existing GCRNs typically generate parameters independently for each node or time step, resulting in massive model parameter counts and high redundancy. To alleviate the aboveissues, an Adaptive Meta-node Graph Convolutional Recurrent Network (AMGCRN) was constructed by leveraging the advantages of meta-nodes in reducing parameter redundancy, as well as the capability of hypernetworks to adapt to spatio-temporal heterogeneity while balancing performance and efficiency. First, a small set of learnable meta-nodes was introduced to distill differentiated traffic patterns. Then, a hypernetwork was employed to dynamically generate node-specific convolutional parameters for different time steps, thereby allowing spatio-temporal heterogeneity to be accommodated while parameter redundancy was effectively reduced. In addition, a multi-head cross-attention mechanism was incorporated to enhance the capture of global temporal dependencies. Experimental results on multiple real-world traffic datasets demonstrate that, compared to Meta Attentive Graph Convolutional Recurrent Network (MAGCRN), the proposed model achievesa relative reduction of approximately 1%-3% in terms of MAE (Mean Absolute Error). Furthermore, in the efficiency evaluation on the PeMSD8 dataset, the proposed model reduces the number of parameters by 83% and FLOPs by 22% compared with MAGCRN, thereby realizing a comprehensive optimization across model capacity, computational complexity, and predictive performance.

Key words: traffic flow prediction, Graph Convolutional Recurrent Network (GCRN), meta-node, hypernetwork, attention mechanism