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News/FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
arxiv
PublishedApril 16, 2026 at 4:00 AM
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FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.13453v1 Announce Type: new Abstract: Traffic forecasting requires modeling complex temporal dynamics and long-range spatial dependencies over large sensor networks. Existing methods typically face a trade-off between expressiveness and efficiency: Transformer-based models capture global d

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Discussion
Mentioned models
04
  • 01
    Transformer
  • 02
    FAST
  • 03
    Mamba
  • 04
    GNN
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#traffic forecasting#spatiotemporal modeling#state-space models#attention mechanisms

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Mentioned models
04
  • 01
    Transformer
  • 02
    FAST
  • 03
    Mamba
  • 04
    GNN
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#traffic forecasting#spatiotemporal modeling#state-space models#attention mechanisms

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