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

Haiyang Liu, Chunjiang Zhu, Detian Zhang

TL;DR

Traffic flow forecasting requires modeling evolving spatial-temporal dependencies on road networks. This work presents GA-STGRN, a framework that augments spatial-temporal graph recurrent networks with a global awareness layer built from three GST^2 architectures and a sequence-aware graph learning module to capture time-varying graphs. The three GST^2 designs—$\mathrm{PGST^2}$, $\mathrm{SGST^2}$, and $\mathrm{FGST^2}$—integrate with STGRNs to deliver improved accuracy across four real PeMS datasets and accelerate convergence. Ablation studies show the value of dynamic adjacency, time embeddings, and the global attention mechanisms, underscoring the framework’s effectiveness for real-time traffic prediction. Overall, GA-STGRN offers a versatile, globally-aware enhancement to STGRNs with meaningful impact for intelligent transportation systems.

Abstract

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.

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

TL;DR

Traffic flow forecasting requires modeling evolving spatial-temporal dependencies on road networks. This work presents GA-STGRN, a framework that augments spatial-temporal graph recurrent networks with a global awareness layer built from three GST^2 architectures and a sequence-aware graph learning module to capture time-varying graphs. The three GST^2 designs—, , and —integrate with STGRNs to deliver improved accuracy across four real PeMS datasets and accelerate convergence. Ablation studies show the value of dynamic adjacency, time embeddings, and the global attention mechanisms, underscoring the framework’s effectiveness for real-time traffic prediction. Overall, GA-STGRN offers a versatile, globally-aware enhancement to STGRNs with meaningful impact for intelligent transportation systems.

Abstract

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.
Paper Structure (25 sections, 15 equations, 8 figures, 5 tables)

This paper contains 25 sections, 15 equations, 8 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traffic Flow Prediction
  4. Graph Convolution Networks
  5. Attention Mechanisms
  6. Proposed Methods
  7. Problem Definition
  8. Sequence-Aware Graph Convolution Network (SGCN)
  9. Spatial-Temporal Graph Recurrent Network
  10. Global Spatial-Temporal Transformer-like Layer
  11. Parallel $\mathrm{GST^2}$
  12. Serial $\mathrm{GST^2}$
  13. Fused $\mathrm{GST^2}$
  14. Output Layer
  15. Experimental Results
  16. ...and 10 more sections

Figures (8)

  • Figure 1: Graph constructions. The underlying road network graphs at different time steps are not fixed but evolving.
  • Figure 2: Frameworks: STGRN and GA-STGRN.
  • Figure 3: Model Overview of $\mathrm{GST^2}$-STGRN.
  • Figure 4: Global Spatial-Temporal Transformer-like architectures ($\mathrm{GST^2}$). (a), (b) and (c) are three different ways of constructing $\mathrm{GST^2}$, and (d) is a component of $\mathrm{FGST^2}$: Spatial-Temporal Fusion Attention (STFA).
  • Figure 5: Applying our new framework in typical STGRNs
  • ...and 3 more figures