Cargando…
A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic
The COVID-19 pandemic is a major global public health problem that has caused hardship to people’s normal production and life. Predicting the traffic revitalization index can provide references for city managers to formulate policies related to traffic and epidemic prevention. Previous methods have...
Autores principales: | Wang, Yue, Lv, Zhiqiang, Sheng, Zhaoyu, Sun, Haokai, Zhao, Aite |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212927/ http://dx.doi.org/10.1016/j.aei.2022.101678 |
Ejemplares similares
-
Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index()
por: Lv, Zhiqiang, et al.
Publicado: (2021) -
A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic
por: Li, Guangquan, et al.
Publicado: (2023) -
A new approach to COVID-19 data mining: A deep spatial–temporal prediction model based on tree structure for traffic revitalization index
por: Lv, Zhiqiang, et al.
Publicado: (2023) -
Changes in the epidemiology of patients hospitalized in France with deep venous thrombosis and pulmonary embolism during the COVID-19 pandemic
por: Gabet, Amélie, et al.
Publicado: (2021) -
Deep learning model for measurement of shoulder critical angle and acromion index on shoulder radiographs
por: Shariatnia, M. Moein, et al.
Publicado: (2022)