Spatial-temporal forecasting has attracted tremendous attention in a wide
range of applications, and traffic flow prediction is a canonical and typical
example. The complex and long-range spatial-temporal correlations of traffic
flow bring it to a most intractable challenge. Existing works typically utilize
shallow graph convolution networks (GNNs) and temporal extracting modules to
model spatial and temporal dependencies respectively. However, the
representation ability of such models is limited due to: (1) shallow GNNs are
incapable to capture long-range spatial correlations, (2) only spatial
connections are considered and a mass of semantic connections are ignored,
which are of great importance for a comprehensive understanding of traffic
networks. To this end, we propose Spatial-Temporal Graph Ordinary Differential
Equation Networks (STGODE). Specifically, we capture spatial-temporal dynamics
through a tensor-based ordinary differential equation (ODE), as a result,
deeper networks can be constructed and spatial-temporal features are utilized
synchronously. To understand the network more comprehensively, semantical
adjacency matrix is considered in our model, and a well-design temporal
dialated convolution structure is used to capture long term temporal
dependencies. We evaluate our model on multiple real-world traffic datasets and
superior performance is achieved over state-of-the-art baselines.

本文提出了一种基于张量的常微分方程网络模型来对交通流进行空时预测，并引入了语义邻接矩阵和时态扩张卷积结构以应对传统模型的表达能力不足和建模不准确的问题。在多个真实数据集上的实验结果表明，该模型取得了领先于现有基准的更好的预测结果。