Utilizing Multihead Attention‐Based Graph Convolution Networks for Traffic Speed Prediction

Accurate traffic speed prediction holds immense significance in mitigating traffic congestion and enhancing traffic safety. However, traffic data exhibit distinct patterns across different cycles (such as weekdays, weekends, and holidays), making it challenging for traditional models to effectively capture this multiperiod heterogeneity in traffic data. Furthermore, most existing research on traffic speed prediction struggles to efficiently capture the spatiotemporal characteristics of dynamic traffic data simultaneously. To tackle these challenges, this paper first introduces spatiotemporal‐aware position encoding (STAPE) technology, which addresses the multiperiod heterogeneity in traffic data by integrating temporal cycle information with spatial position information. Second, a multilevel spatiotemporal feature extraction architecture is designed, leveraging graph convolutional network (GCN) to capture the topological structure and spatial features of the traffic road network. By applying gated recurrent unit (GRU) to capture the temporal dependencies of traffic data, and combining GCN and GRU in multiple stages, this architecture deeply explores the spatiotemporal features of traffic data. Additionally, this paper integrates a multihead attention mechanism, which, in conjunction with the parallelized attention channel adaptive mechanism and the multilevel spatiotemporal feature extraction architecture, enhances the model’s ability to adaptively model different spatiotemporal patterns dynamically, thereby efficiently capturing the dynamically changing spatiotemporal features. Extensive performance evaluation experiments conducted on the METR‐LA and PEMS‐BAY datasets demonstrate that the predictive performance of the proposed model surpasses that of nine other baseline methods.