STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting

• URL: http://arxiv.org/abs/2508.16685v1
• Date: Thu, 21 Aug 2025 17:21:14 GMT
• Title: STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
• Authors: Zhuding Liang, Jianxun Cui, Qingshuang Zeng, Feng Liu, Nenad Filipovic, Tijana Geroski,
• Abstract summary: This paper presents a novel deep learning model, the Spatial-Temporal Unified Graph Attention Network (STGAtt)<n>By leveraging a unified graph representation and an attention mechanism, STGAtt effectively captures complex spatial-temporal dependencies.
• Score: 3.9560660144028126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and timely traffic flow forecasting is crucial for intelligent transportation systems. This paper presents a novel deep learning model, the Spatial-Temporal Unified Graph Attention Network (STGAtt). By leveraging a unified graph representation and an attention mechanism, STGAtt effectively captures complex spatial-temporal dependencies. Unlike methods relying on separate spatial and temporal dependency modeling modules, STGAtt directly models correlations within a Spatial-Temporal Unified Graph, dynamically weighing connections across both dimensions. To further enhance its capabilities, STGAtt partitions traffic flow observation signal into neighborhood subsets and employs a novel exchanging mechanism, enabling effective capture of both short-range and long-range correlations. Extensive experiments on the PEMS-BAY and SHMetro datasets demonstrate STGAtt's superior performance compared to state-of-the-art baselines across various prediction horizons. Visualization of attention weights confirms STGAtt's ability to adapt to dynamic traffic patterns and capture long-range dependencies, highlighting its potential for real-world traffic flow forecasting applications.

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