Deep graph convolution networks (GCNs) have recently shown excellent performance in traffic prediction tasks. However, they face some challenges. First, few existing models consider the influence of auxiliary information, i.e., weather and holidays, which may result in a poor grasp of spatial-temporal dynamics of traffic data. Second, both the construction of a dynamic adjacent matrix and regular graph convolution operations have quadratic computation complexity, which restricts the scalability of GCN-based models. To address such challenges, this work proposes a deep encoder-decoder model entitled AIMSAN. It contains an auxiliary information-aware module (AIM) and sparse cross attention-based graph convolution network (SAN). The former learns multi-attribute auxiliary information and obtains its embedded presentation of different time-window sizes. The latter uses a cross-attention mechanism to construct dynamic adjacent matrices by fusing traffic data and embedded auxiliary data. Then, SAN applies diffusion GCN on traffic data to mine rich spatial-temporal dynamics. Furthermore, AIMSAN considers and uses the spatial sparseness of traffic nodes to reduce the quadratic computation complexity. Experimental results on three public traffic datasets demonstrate that the proposed method outperforms other counterparts in terms of various performance indices. Specifically, the proposed method has competitive performance with the state-of-the-art algorithms but saves 35.74% of GPU memory usage, 42.25% of training time, and 45.51% of validation time on average.

翻译：深度图卷积网络（GCN）近年来在交通预测任务中展现出卓越性能。然而，这些模型仍面临若干挑战。首先，现有模型鲜有考虑天气、节假日等辅助信息的影响，导致难以准确把握交通数据的时空动态特性。其次，动态邻接矩阵的构建与常规图卷积运算均具有二次计算复杂度，这限制了基于GCN的模型的可扩展性。为应对上述挑战，本文提出一种名为AIMSAN的深度编码器-解码器模型。该模型包含辅助信息感知模块（AIM）和基于稀疏交叉注意力的图卷积网络（SAN）。前者学习多属性辅助信息并获取不同时间窗大小的嵌入表示；后者通过交叉注意力机制融合交通数据与嵌入的辅助数据来构建动态邻接矩阵。随后，SAN在交通数据上应用扩散GCN以挖掘丰富的时空动态特征。此外，AIMSAN利用交通节点的空间稀疏性来降低二次计算复杂度。在三个公开交通数据集上的实验结果表明，所提方法在多种性能指标上均优于其他对比方法。具体而言，该方法具有与现有最优算法相媲美的性能，但平均可节省35.74%的GPU显存、42.25%的训练时间和45.51%的验证时间。