Hierarchical Dynamic Spatio-Temporal Graph Convolutional Networks with Self-Supervised Learning for Traffic Flow Forecasting
It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlations. However, existing approaches predominantly foc...
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| Principais autores: | , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
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MDPI AG
2024-09-01
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| coleção: | Inventions |
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| Acesso em linha: | https://www.mdpi.com/2411-5134/9/5/102 |
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