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SIMBAD: Spatio-Temporal Traffic Forecasting Robust to Aperiodicity — arXiv:2611.09015

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.09015
License (as archived): CC0-1.0
Archived: 2026-11-09T22:33:51.200Z
SHA-256 of the archived text: ebf088a4e1bfc0f11cc1fa8263d9761d33939bba13211c73e90e6e1367db023f
Stable URL of this page: https://metraipolis.com/documento/dc_fafacbb4bb50

How to cite

SIMBAD: Spatio-Temporal Traffic Forecasting Robust to Aperiodicity — arXiv:2611.09015 — arXiv (en), https://arxiv.org/abs/2611.09015 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fafacbb4bb50 (Metraipolis archive dc_fafacbb4bb50, sha256 ebf088a4e1bfc0f11cc1fa8263d9761d33939bba13211c73e90e6e1367db023f)

BibTeX

@misc{dcfafacbb4bb50,
  title = {SIMBAD: Spatio-Temporal Traffic Forecasting Robust to Aperiodicity — arXiv:2611.09015},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.09015},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fafacbb4bb50; sha256 ebf088a4e1bfc0f11cc1fa8263d9761d33939bba13211c73e90e6e1367db023f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.09015. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate traffic prediction is essential for urban planning but remains challenging due to the irregular patterns caused by unexpected events. While Spatio-Temporal Graph Neural Networks (STGNNs) effectively model periodic data, they often struggle with these aperiodic fluctuations. To address this, we propose SIMBAD, which improves generalization irregular patterns via two key mechanisms: 1) adaptive regulation of periodic signals to prioritize recent signals, and 2) dynamic spatial modeling that adjusts influence between connected nodes based on contextual relations. Built upon simple similarity metrics to adapt to aperiodic patterns, SIMBAD outperforms existing methods on real-world benchmark datasets, demonstrating superior performance in predicting unseen and irregular traffic events.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.09015 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-09T22:30:47.262Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).