Proceedings

HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting

Yongyao Wang, Xie Yu, Jingyuan Wang, Jiahao Ji, Li Chao

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. AI4Tech: AI Enabling Technologies. Pages 6555-6563.

https://doi.org/10.24963/ijcai.2026/729

Keywords

  • Domain-specific AI4Tech: AI4Transport

Abstract

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.

Yongyao Wang, Xie Yu, Jingyuan Wang, Jiahao Ji, and Li Chao. 2026. HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 6555–6563. https://doi.org/10.24963/ijcai.2026/729
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