  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.
