TEDDGN: a trend-event decoupled dynamic graph network for traffic forecasting
Accurate traffic flow prediction is of great significance for urban planning and traffic management. Among existing methods, researchers have shown remarkable progress by utilizing spatiotemporal networks. Unfortunately, most of these methods ignore the heterogeneity of multi-scale features in traff...
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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Taylor & Francis Group
2026-01-01
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| Serie: | International Journal of Digital Earth |
| Soggetti: | |
| Accesso online: | https://www.tandfonline.com/doi/10.1080/17538947.2026.2656543 |
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