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.

翻译：本文研究交通流预测问题，旨在基于路网及历史交通状况预测未来交通状态。该问题通常通过利用时空图神经网络建模交通数据中的复杂时空关联来解决。然而，由于图神经网络在应对复杂交通网络时表示能力有限，此类方法的性能仍不尽如人意。图本质上难以捕捉非成对关系。更糟糕的是，现有方法遵循线性聚合邻域信息的消息传递范式，无法捕捉复杂的时空高阶交互。为解决这些问题，本文提出一种名为动态超图结构学习的新型交通流预测模型。为学习非成对关系，DyHSL提取超图结构信息以建模交通网络动态性，并通过聚合关联超边的消息更新每个节点表征。此外，为捕捉路网中的高阶时空关联，我们引入交互图卷积模块，进一步建模每个节点的邻域交互。最后，将这两个视角整合到整体多尺度关联提取模块中，该模块通过不同尺度的时序池化来建模不同的时间模式。在四个主流交通基准数据集上的大量实验表明，与各类竞争基线相比，我们提出的DyHSL具有优越性能。