This paper studies the problem of traffic flow forecasting, which aims to
predict future traffic conditions on the basis of road networks and traffic
conditions in the past. The problem is typically solved by modeling complex
spatio-temporal correlations in traffic data using spatio-temporal graph neural
networks (GNNs). However, the performance of these methods is still far from
satisfactory since GNNs usually have limited representation capacity when it
comes to complex traffic networks. Graphs, by nature, fall short in capturing
non-pairwise relations. Even worse, existing methods follow the paradigm of
message passing that aggregates neighborhood information linearly, which fails
to capture complicated spatio-temporal high-order interactions. To tackle these
issues, in this paper, we propose a novel model named Dynamic Hypergraph
Structure Learning (DyHSL) for traffic flow prediction. To learn non-pairwise
relationships, our DyHSL extracts hypergraph structural information to model
dynamics in the traffic networks, and updates each node representation by
aggregating messages from its associated hyperedges. Additionally, to capture
high-order spatio-temporal relations in the road network, we introduce an
interactive graph convolution block, which further models the neighborhood
interaction for each node. Finally, we integrate these two views into a
holistic multi-scale correlation extraction module, which conducts temporal
pooling with different scales to model different temporal patterns. Extensive
experiments on four popular traffic benchmark datasets demonstrate the
effectiveness of our proposed DyHSL compared with a broad range of competing
baselines.

本文研究了交通流预测问题，通过建模交通网络和过去的交通状况来预测未来的交通情况。为了解决现有方法在复杂交通网络中表现不佳的问题，本文提出了一种名为动态超图结构学习 (DyHSL) 的新模型，该模型通过提取超图结构信息来学习非成对关系，并通过聚合来自关联超边的消息来更新每个节点的表示。为了捕捉道路网络中的高阶时空关系，介绍了互动图卷积块，进一步模拟每个节点的邻域交互。最后，将这两个视角整合到一个综合的多尺度相关性提取模块中，该模块通过不同尺度的时间池化来建模不同的时间模式。对四个流行的交通基准数据集进行了大量实验证明了我们提出的 DyHSL 与广泛的竞争基准相比的有效性。