提出一种结合多头注意力(multi-head attention,MHA)机制和自适应邻接矩阵的新型时空图卷积网络(spatio-temporal graphconvolutional network,STGCN)模型.MHA机制对时空特征和外部环境因素进行加权融合,自适应邻接矩阵对道路网络的连接权重进行动态调整,提升了对空间依赖性的刻画能力.结果表明,该模型在伦敦道路网络数据集上的表现优于已有模型,在多个指标上显著提升了预测精度.
This paper proposed a novel spatio-temporal graph convolutional network (STGCN) enhanced with a multi-head attention (MHA) mechanism and adaptive adjacency matrices. The MHA mechanism dynamically weighted spatio-temporal features and external environmental factors, while the adaptive adjacency matrices adjusted the connection weights of road networks to improve the model's ability to capture spatial dependencies. Experimental results demonstrated that the model outperformed state-of-the-art (SOTA) models on the London road network dataset, achieving significant improvements across multiple evaluation metrics.
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