论文标题
ISTD-GCN：迭代时空扩散图卷积网络，用于交通速度预测
ISTD-GCN: Iterative Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Speed Forecasting
论文作者
论文摘要
用于交通速度的大多数现有算法预测拆分空间特征和时间特征与独立模块，然后从两个维度关联信息。但是，空间和时间维度的特征会影响相互影响，分离的提取物隔离了这种依赖性，并可能导致结果不准确。在本文中，我们将信息扩散到模型空间特征和时间特征的视角同步。直观地，顶点不仅将信息传播到邻里，而且还传达了随后的状态以及时间维度。因此，我们可以将这种异质时空结构建模为均匀的扩散过程。在此基础上，我们提出了一个迭代时空扩散图卷积网络（ISTD-GCN），以同步提取空间和时间特征，因此可以更好地建模这两个维度之间的依赖性。两个流量数据集的实验表明，我们的ISTD-GCN在交通速度预测任务中的表现优于10个基准。源代码可在https://github.com/Anonymous上找到。
Most of the existing algorithms for traffic speed forecasting split spatial features and temporal features to independent modules, and then associate information from both dimensions. However, features from spatial and temporal dimensions influence mutually, separated extractions isolate such dependencies, and might lead to inaccurate results. In this paper, we incorporate the perspective of information diffusion to model spatial features and temporal features synchronously. Intuitively, vertices not only diffuse information to the neighborhood but also to the subsequent state along with the temporal dimension. Therefore, we can model such heterogeneous spatial-temporal structures as a homogeneous process of diffusion. On this basis, we propose an Iterative Spatial-Temporal Diffusion Graph Convolutional Network (ISTD-GCN) to extract spatial and temporal features synchronously, thus dependencies between both dimensions can be better modeled. Experiments on two traffic datasets illustrate that our ISTD-GCN outperforms 10 baselines in traffic speed forecasting tasks. The source code is available at https://github.com/Anonymous.
