Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the...

Descripción completa

Detalles Bibliográficos
Autores principales: Yu, Kun, Qin, Xizhong, Jia, Zhenhong, Du, Yan, Lin, Mengmeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708434/
https://www.ncbi.nlm.nih.gov/pubmed/34960560
http://dx.doi.org/10.3390/s21248468

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708434/
https://www.ncbi.nlm.nih.gov/pubmed/34960560
http://dx.doi.org/10.3390/s21248468

Ejemplares similares