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Traffic flow prediction plays a crucial role in alleviating trafficcongestion and enhancing transport efficiency. While combining graphconvolution networks with recurrent neural networks for spatial-temporalmodeling is a common strategy in this realm, the restricted structure ofrecurrent neural networks limits their ability to capture global information.For spatial modeling, many prior studies learn a graph structure that isassumed to be fixed and uniform at all time steps, which may not be true. Thispaper introduces a novel traffic prediction framework, Global-Aware EnhancedSpatial-Temporal Graph Recurrent Network (GA-STGRN), comprising two corecomponents: a spatial-temporal graph recurrent neural network and a globalawareness layer. Within this framework, three innovative prediction models areformulated. A sequence-aware graph neural network is proposed and integratedinto the Gated Recurrent Unit (GRU) to learn non-fixed graphs at different timesteps and capture local temporal relationships. To enhance the model's globalperception, three distinct global spatial-temporal transformer-likearchitectures (GST^2) are devised for the global awareness layer. We conductextensive experiments on four real traffic datasets and the results demonstratethe superiority of our framework and the three concrete models.

Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction