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Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction
Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results q...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055944/ https://www.ncbi.nlm.nih.gov/pubmed/36991611 http://dx.doi.org/10.3390/s23062897 |