When considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an important step towards achieving accurate traffic forecasting. The impacts of external factors on the traffic flow have complex correlations. However, existing studies seldom consider external factors or neglecting the effect of the complex correlations among external factors on traffic. Intuitively, knowledge graphs can naturally describe these correlations, but knowledge graphs and traffic networks are essentially heterogeneous networks; thus, it is a challenging problem to integrate the information in both networks. We propose a knowledge representation-driven traffic forecasting method based on spatiotemporal graph convolutional networks. We first construct a city knowledge graph for traffic forecasting, then use KS-Cells to combine the information from the knowledge graph and the traffic network, and finally, capture the temporal changes of the traffic state with GRU. Testing on real-world datasets shows that the KST-GCN has higher accuracy than the baseline traffic forecasting methods at various prediction horizons. We provide a new way to integrate knowledge and the spatiotemporal features of data for traffic forecasting tasks. Without any loss of generality, the proposed method can also be extended to other spatiotemporal forecasting tasks.

中文翻译：

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KST-GCN：知识驱动的时空图卷积网络，用于交通量预测

考虑交通的时空特征时，捕获各种外部因素对旅行的影响是实现准确交通预测的重要一步。外部因素对交通流量的影响具有复杂的相关性。但是，现有的研究很少考虑外部因素，或者忽略了外部因素之间的复杂关联对交通的影响。直觉上，知识图自然可以描述这些相关性，但是知识图和交通网络本质上是异构网络。因此，在两个网络中集成信息是一个具有挑战性的问题。我们提出了一种基于时空图卷积网络的知识表示驱动的交通量预测方法。我们首先构建用于交通预测的城市知识图，然后使用KS-Cells组合知识图和交通网络中的信息，最后使用GRU捕获交通状态的时间变化。对现实数据集的测试表明，在各种预测范围内，KST-GCN比基线流量预测方法具有更高的准确性。我们提供了一种新的方法来集成知识和数据的时空特征，以进行流量预测任务。不失一般性，所提出的方法还可以扩展到其他时空预测任务。我们提供了一种新的方法来集成知识和数据的时空特征，以进行流量预测任务。不失一般性，所提出的方法还可以扩展到其他时空预测任务。我们提供了一种新的方法来集成知识和数据的时空特征，以进行流量预测任务。不失一般性，所提出的方法还可以扩展到其他时空预测任务。