用分层MLP架构实现高效大尺度交通预测。
HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting
- 分层时空混合块实现自底向上聚合与自顶向下传播。
- 在4个真实数据集上达到领先性能，计算效率高。
- 适合需要实时处理大规模交通数据的场景。
交通预测对现代城市治理至关重要。近年来，大尺度预测受到关注，因其更贴近真实交通网络的复杂性。然而，现有模型常具有二次计算复杂度，难以应用于大规模现实场景。本文提出新型框架——时空分层混合器（HieraMix），采用全MLP架构实现高效且有效的大型交通预测。HieraMix通过分层时空混合块，实现自底向上的特征聚合与自顶向下的信息传播，以提取多分辨率特征。此外，自适应区域混合器基于区域语义生成变换矩阵，使模型能动态捕捉不同区域随时间演化的时空模式。在四个大规模真实数据集上的广泛实验表明，该方法不仅达到当前最优性能，还具备优异的计算效率。
原文摘要 · Abstract (English)
Traffic forecasting task is significant to modern urban management. Recently, there is growing attention on large-scale forecasting, as it better reflects the complexity of real-world traffic networks. However, existing models often exhibit quadratic computational complexity, making them impractical for large-scale real-world scenarios. In this paper, we propose a novel framework, Spatio-Temporal Hierarchical Mixer (HieraMix), which leverages an all-MLP architecture for efficient and effective large-scale traffic forecasting. HieraMix employs a hierarchical spatiotemporal mixing block to extract multi-resolution features through bottom-up aggregation and top-down propagation. Furthermore, an adaptive region mixer generates transformation matrices based on regional semantics, enabling our model to dynamically capture evolving spatiotemporal patterns for different regions. Extensive experiments conducted on four large-scale real-world datasets demonstrate that the proposed method not only achieves state-of-the-art performance but also exhibits competitive computational efficiency.
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