用于交通流预测的动态超图结构学习
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting
AI总结：
本文提出动态超图结构学习模型DyHSL，通过超图建模非成对关系、交互式图卷积捕捉高阶时空交互，并结合多尺度时间池化，在四个交通基准数据集上显著提升交通流预测性能。
摘要：
本文研究交通流预测问题，其目标是根据道路网络和过去的交通状况预测未来的交通状态。该问题通常通过使用时空图神经网络（GNN）对交通数据中复杂的时空相关性进行建模来解决。然而，这些方法的性能仍远不能令人满意，因为GNN在处理复杂交通网络时通常表示能力有限。图本质上难以捕捉非成对关系。更糟糕的是，现有方法遵循消息传递范式，以线性方式聚合邻域信息，无法捕捉复杂的时空高阶交互。为了解决这些问题，本文提出了一种名为动态超图结构学习（DyHSL）的新型交通流预测模型。为了学习非成对关系，我们的DyHSL提取超图结构信息来建模交通网络中的动态，并通过聚合来自其关联超边的消息来更新每个节点的表示。此外，为了捕捉道路网络中的高阶时空关系，我们引入了一个交互式图卷积块，进一步对每个节点的邻域交互进行建模。最后，我们将这两个视角整合到一个整体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。在四个流行的交通基准数据集上的大量实验表明，与广泛的竞争基线相比，我们提出的DyHSL具有有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
