用统一图结构+注意力机制，精准预测交通流量变化。
STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
- 构建时空统一图，动态加权空间与时间关联。
- 在两个真实数据集上优于现有模型，长短期依赖捕捉更优。
- 适合交通规划、智慧出行等实时决策场景使用。
准确及时的交通流量预测对智能交通系统至关重要。本文提出一种新型深度学习模型——时空统一图注意力网络（STGAtt）。通过统一图表示与注意力机制，STGAtt有效捕捉复杂的时空依赖关系。不同于依赖独立空间与时间建模模块的方法，STGAtt直接在时空统一图中建模相关性，并动态调整跨维度连接权重。为进一步提升性能，该模型将交通流量观测信号划分为邻域子集，并引入新颖的交互机制，以有效捕获短程与长程相关性。在PEMS-BAY和SHMetro数据集上的大量实验表明，STGAtt在多种预测时长下均显著优于当前先进基线模型。注意力权重可视化结果证实其能适应动态交通模式，有效捕捉长程依赖，展现出在真实交通预测应用中的潜力。
原文摘要 · Abstract (English)
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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