Traffic flow prediction plays a crucial role in alleviating traffic
congestion and enhancing transport efficiency. While combining graph
convolution networks with recurrent neural networks for spatial-temporal
modeling is a common strategy in this realm, the restricted structure of
recurrent neural networks limits their ability to capture global information.
For spatial modeling, many prior studies learn a graph structure that is
assumed to be fixed and uniform at all time steps, which may not be true. This
paper introduces a novel traffic prediction framework, Global-Aware Enhanced
Spatial-Temporal Graph Recurrent Network (GA-STGRN), comprising two core
components: a spatial-temporal graph recurrent neural network and a global
awareness layer. Within this framework, three innovative prediction models are
formulated. A sequence-aware graph neural network is proposed and integrated
into the Gated Recurrent Unit (GRU) to learn non-fixed graphs at different time
steps and capture local temporal relationships. To enhance the model's global
perception, three distinct global spatial-temporal transformer-like
architectures (GST^2) are devised for the global awareness layer. We conduct
extensive experiments on four real traffic datasets and the results demonstrate
the superiority of our framework and the three concrete models.

该论文介绍了一种新颖的交通预测框架，它包括两个核心组件：空间 - 时间图递归神经网络和全局感知层。通过引入序列感知图神经网络，该模型可以学习不同时间步骤上的非固定图并捕捉局部时间关系。为了增强模型的全局认知，该框架还设计了三种独特的全局空间 - 时间转换器架构。在四个实际交通数据集上进行的广泛实验证明了我们框架和三种具体模型的优越性。