论文标题
贝叶斯时空图形卷积网络，用于交通预测
Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting
论文作者
论文摘要
在流量预测中，图形卷积网络（GCN）以时空图为模型的流量流动，已取得了出色的性能。但是，现有的基于GCN的方法启发式将图形结构定义为道路网络的物理拓扑，而忽略了图结构对流量数据的潜在依赖性。定义的图形结构是确定性的，缺乏对不确定性的研究。在本文中，我们提出了一个贝叶斯时空图形卷积网络（BSTGCN）进行交通预测。我们网络中的图形结构以端到端的方式从道路网络和交通数据的物理拓扑中汲取了借助，从而发现了对流量流之间关系的更准确描述。此外，提出了一个参数生成模型来表示图形结构，从而增强了GCN的概括能力。我们验证了方法对两个现实世界数据集的有效性，并且实验结果表明，与最新方法相比，BSTGCN的性能卓越。
In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as spatio-temporal graphs, have achieved remarkable performance. However, existing GCN-based methods heuristically define the graph structure as the physical topology of the road network, ignoring potential dependence of the graph structure over traffic data. And the defined graph structure is deterministic, which lacks investigation of uncertainty. In this paper, we propose a Bayesian Spatio-Temporal Graph Convolutional Network (BSTGCN) for traffic prediction. The graph structure in our network is learned from the physical topology of the road network and traffic data in an end-to-end manner, which discovers a more accurate description of the relationship among traffic flows. Moreover, a parametric generative model is proposed to represent the graph structure, which enhances the generalization capability of GCNs. We verify the effectiveness of our method on two real-world datasets, and the experimental results demonstrate that BSTGCN attains superior performance compared with state-of-the-art methods.
