Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overlooked the dynamic spatio-temporal correlations among nodes by using the same adjacency matrix across different time steps. To address these limitations, we propose a novel approach called Spatio-Temporal Joint Graph Convolutional Networks (STJGCN) for accurate traffic forecasting on road networks over multiple future time steps. Specifically, our method encompasses the construction of both pre-defined and adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which represent comprehensive and dynamic spatio-temporal correlations. We further introduce dilated causal spatio-temporal joint graph convolution layers on the STJG to capture spatio-temporal dependencies from distinct perspectives with multiple ranges. To aggregate information from different ranges, we propose a multi-range attention mechanism. Finally, we evaluate our approach on five public traffic datasets and experimental results demonstrate that STJGCN is not only computationally efficient but also outperforms 11 state-of-the-art baseline methods.

翻译：近期研究逐渐将交通预测问题建模为时空图建模问题。这类方法通常在每个时间步构建静态空间图，并通过连接相邻时间步中同一节点构建时空图。然而，这种方式未能显式刻画不同时间步上不同节点间的关联性，限制了图神经网络的学习能力。此外，现有模型在不同时间步使用相同邻接矩阵，忽略了节点间动态的时空相关性。为解决上述局限，我们提出一种名为时空联合图卷积网络（STJGCN）的新方法，用于在道路网络上实现多未来时间步的精准交通预测。具体而言，本方法在任意两个时间步间构建预定义和自适应两种时空联合图（STJG），以表征全面且动态的时空相关性。我们在STJG上引入膨胀因果时空联合图卷积层，从多范围、多视角捕获时空依赖关系。为聚合不同范围的信息，提出多范围注意力机制。最后，在五个公开交通数据集上的实验表明，STJGCN不仅计算高效，且整体性能优于11种当前最优基线方法。