STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

Nowadays, as a crucial component of intelligent transportation systems, traffic flow prediction has received extensive concern. However, most of the existing studies extracted spatial-temporal features with modules that do not differentiate with time and space, and failed to consider spatial-tempora...

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Detalles Bibliográficos
Autores principales: Yu, Xian, Bao, Yin-Xin, Shi, Quan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559355/
https://www.ncbi.nlm.nih.gov/pubmed/37809690
http://dx.doi.org/10.1016/j.heliyon.2023.e19927

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559355/
https://www.ncbi.nlm.nih.gov/pubmed/37809690
http://dx.doi.org/10.1016/j.heliyon.2023.e19927

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