A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index

doi:10.1016/j.datak.2023.102193

	A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index
	Lv, Zhiqiang 1,3; Wang, Xiaotong 1; Cheng, Zesheng 1; Li, Jianbo 1; Li, Haoran 1,2; Xu, Zhihao 1,2
	2023-07-01
发表期刊	DATA & KNOWLEDGE ENGINEERING
ISSN	0169-023X
卷号	146 页码:18
摘要	The outbreak of the COVID-19 epidemic has had a huge impact on a global scale and its impact has covered almost all human industries. The Chinese government enacted a series of policies to restrict the transportation industry in order to slow the spread of the COVID-19 virus in early 2020. With the gradual control of the COVID-19 epidemic and the reduction of confirmed cases, the Chinese transportation industry has gradually recovered. The traffic revitalization index is the main indicator for evaluating the degree of recovery of the urban transportation industry after being affected by the COVID-19 epidemic. The prediction research of traffic revitalization index can help the relevant government departments to know the state of urban traffic from the macro level and formulate relevant policies. Therefore, this study proposes a deep spatial- temporal prediction model based on tree structure for the traffic revitalization index. The model mainly includes spatial convolution module, temporal convolution module and matrix data fusion module. The spatial convolution module builds a tree convolution process based on the tree structure that can contain directional features and hierarchical features of urban nodes. The temporal convolution module constructs a deep network for capturing temporal dependent features of the data in the multi-layer residual structure. The matrix data fusion module can perform multi-scale fusion of COVID-19 epidemic data and traffic revitalization index data to further improve the prediction effect of the model. In this study, experimental comparisons between our model and multiple baseline models are conducted on real datasets. The experimental results show that our model has an average improvement of 21%, 18%, and 23% in MAE, RMSE and MAPE indicators, respectively.
关键词	COVID-19 Traffic revitalization index Spatial-temporal model Directional feature Hierarchical feature
DOI	10.1016/j.datak.2023.102193
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan Key Special Projects[2018YFB2100303] ; Shandong Province colleges and universities youth innovation technology plan innovation team project[2020KJN011] ; Program for Innovative Postdoctoral Talents in Shandong Province[40618030001] ; National Natural Science Foundation of China[61802216] ; Postdoctoral Science Foundation of China[2018M642613]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:001011450500001
出版者	ELSEVIER
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21242
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cheng, Zesheng
作者单位	1.Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China 2.Inst Ubiquitous Networks & Urban Comp, Qingdao 266070, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Lv, Zhiqiang,Wang, Xiaotong,Cheng, Zesheng,et al. A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index[J]. DATA & KNOWLEDGE ENGINEERING,2023,146:18.
APA	Lv, Zhiqiang,Wang, Xiaotong,Cheng, Zesheng,Li, Jianbo,Li, Haoran,&Xu, Zhihao.(2023).A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index.DATA & KNOWLEDGE ENGINEERING,146,18.
MLA	Lv, Zhiqiang,et al."A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index".DATA & KNOWLEDGE ENGINEERING 146(2023):18.