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...

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Detalles Bibliográficos
Autores principales: Gu, Junhua, Jia, Zhihao, Cai, Taotao, Song, Xiangyu, Mahmood, Adnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055944/
https://www.ncbi.nlm.nih.gov/pubmed/36991611
http://dx.doi.org/10.3390/s23062897

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