A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index
文献类型：期刊论文
| 作者 | Lv, Zhiqiang2,3; Wang, Xiaotong2; Cheng, Zesheng2; Li, Jianbo2; Li, Haoran1,2; Xu, Zhihao1,2 |
| 刊名 | DATA & KNOWLEDGE ENGINEERING
 |
| 出版日期 | 2023-07-01 |
| 卷号 | 146页码:18 |
| ISSN号 | 0169-023X |
| 关键词 | COVID-19 Traffic revitalization index Spatial-temporal model Directional feature Hierarchical feature |
| DOI | 10.1016/j.datak.2023.102193 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:001011450500001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21241]  |
| 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.Inst Ubiquitous Networks & Urban Comp, Qingdao 266070, Peoples R China 2.Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, 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. |
入库方式： OAI收割
来源：计算技术研究所
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