ADSTGCN: A Dynamic Adaptive Deeper Spatio-Temporal Graph Convolutional Network for Multi-Step Traffic Forecasting

Multi-step traffic forecasting has always been extremely challenging due to constantly changing traffic conditions. Advanced Graph Convolutional Networks (GCNs) are widely used to extract spatial information from traffic networks. Existing GCNs for traffic forecasting are usually shallow networks th...

Descripción completa

Detalles Bibliográficos
Autores principales: Cui, Zhengyan, Zhang, Junjun, Noh, Giseop, Park, Hyun Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422259/
https://www.ncbi.nlm.nih.gov/pubmed/37571733
http://dx.doi.org/10.3390/s23156950

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422259/
https://www.ncbi.nlm.nih.gov/pubmed/37571733
http://dx.doi.org/10.3390/s23156950

Ejemplares similares