Accurate forecasting of citywide traffic flow has been playing critical role
in a variety of spatial-temporal mining applications, such as intelligent
traffic control and public risk assessment. While previous work has made
significant efforts to learn traffic temporal dynamics and spatial
dependencies, two key limitations exist in current models. First, only the
neighboring spatial correlations among adjacent regions are considered in most
existing methods, and the global inter-region dependency is ignored.
Additionally, these methods fail to encode the complex traffic transition
regularities exhibited with time-dependent and multi-resolution in nature. To
tackle these challenges, we develop a new traffic prediction
framework-Spatial-Temporal Graph Diffusion Network (ST-GDN). In particular,
ST-GDN is a hierarchically structured graph neural architecture which learns
not only the local region-wise geographical dependencies, but also the spatial
semantics from a global perspective. Furthermore, a multi-scale attention
network is developed to empower ST-GDN with the capability of capturing
multi-level temporal dynamics. Experiments on several real-life traffic
datasets demonstrate that ST-GDN outperforms different types of
state-of-the-art baselines. Source codes of implementations are available at
this https URL

为了更准确地预测城市交通流量并在智能交通管制和公共风险评估等方面实现更有效的空间 - 时间挖掘应用，我们开发了一种新的交通预测框架 Spatial-Temporal Graph Diffusion Network（ST-GDN），该框架具有层次结构的图神经架构，不仅可以从局部角度学习区域地理依赖关系，而且可以从全局角度学习空间语义。该框架还配备了多尺度注意力网络，以提高其捕捉多级时间动态的能力。多项实验结果表明 ST-GDN 的性能优于多种最新基线。