用动态图注意力解决交通数据块缺失填补难题
STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
- 引入专家混合框架捕捉时空特征与影响权重
- 设计低秩采样图注意力机制，动态建模道路网关联
- 在4个数据集上优于现有最优方法，适合交通流补全场景
交通数据填补对智能交通系统中的流量预测等应用至关重要。现有时序到空间的序列方法在块状缺失场景下难以有效提取特征，且静态图结构限制了模型对非平稳交通数据分布偏移的适应能力。为此，本文提出一种名为STAMImputer的时空注意力专家混合网络。通过引入专家混合（MoE）框架，捕捉潜在的时空特征及其影响权重，有效填补块状缺失数据；设计新颖的低秩引导采样图注意力（LrSGAT）机制，动态平衡路网中的局部与全局相关性，利用采样注意力向量生成实时空间关联的动态图。在四个交通数据集上进行了广泛实验，结果表明，与现有最先进方法相比，STAMImputer显著提升性能。代码已开源。
原文摘要 · Abstract (English)
Traffic data imputation is fundamentally important to support various applications in intelligent transportation systems such as traffic flow prediction. However, existing time-to-space sequential methods often fail to effectively extract features in block-wise missing data scenarios. Meanwhile, the static graph structure for spatial feature propagation significantly constrains the models flexibility in handling the distribution shift issue for the nonstationary traffic data. To address these issues, this paper proposes a SpatioTemporal Attention Mixture of experts network named STAMImputer for traffic data imputation. Specifically, we introduce a Mixture of Experts (MoE) framework to capture latent spatio-temporal features and their influence weights, effectively imputing block missing. A novel Low-rank guided Sampling Graph ATtention (LrSGAT) mechanism is designed to dynamically balance the local and global correlations across road networks. The sampled attention vectors are utilized to generate dynamic graphs that capture real-time spatial correlations. Extensive experiments are conducted on four traffic datasets for evaluation. The result shows STAMImputer achieves significantly performance improvement compared with existing SOTA approaches. Our codes are available at https://github.com/RingBDStack/STAMImupter.
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