Institute of Computing Technology, Chinese Academy IR
A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic | |
Wang, Yue1; Lv, Zhiqiang1,2; Sheng, Zhaoyu3; Sun, Haokai3; Zhao, Aite1 | |
2022-08-01 | |
发表期刊 | ADVANCED ENGINEERING INFORMATICS |
ISSN | 1474-0346 |
卷号 | 53页码:12 |
摘要 | 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 struggled to capture the complex and diverse dynamic spatio-temporal correlations during the COVID-19 pandemic. Therefore, we propose a deep spatio-temporal meta-learning model for the prediction of traffic revitalization index (DeepMeta-TRI) using external auxiliary information such as COVID-19 data. We conduct extensive experiments on a real-world dataset, and the results validate the predictive performance of DeepMeta-TRI and its effectiveness in addressing underfitting. |
关键词 | Urban computing Traffic revitalization index prediction COVID-19pandemic Meta-learning Spatio-temporal correlation |
DOI | 10.1016/j.aei.2022.101678 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62106117] ; Shandong Provin-cial Natural Science Foundation, China[ZR2021QF084] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary |
WOS记录号 | WOS:000841095600007 |
出版者 | ELSEVIER SCI LTD |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/19453 |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Zhao, Aite |
作者单位 | 1.Qingdao Univ, Coll Comp Sci & Technol, Qingdao, Peoples R China 2.Inst Comp Technol, Chinese Acad Sci, Beijing, Peoples R China 3.Inst Ubiquitous Networks & Urban Comp, Qingdao, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Yue,Lv, Zhiqiang,Sheng, Zhaoyu,et al. A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic[J]. ADVANCED ENGINEERING INFORMATICS,2022,53:12. |
APA | Wang, Yue,Lv, Zhiqiang,Sheng, Zhaoyu,Sun, Haokai,&Zhao, Aite.(2022).A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic.ADVANCED ENGINEERING INFORMATICS,53,12. |
MLA | Wang, Yue,et al."A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic".ADVANCED ENGINEERING INFORMATICS 53(2022):12. |
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