research-article

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

Authors:

Zhihao XuAuthors Info & Claims

Volume 146, Issue C

https://doi.org/10.1016/j.datak.2023.102193

Published: 01 July 2023 Publication History

Abstract

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.

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Cited By

Wang BHe XLin HShen GKong X(2024)An Epidemic Trend Prediction Model with Multi-source Auxiliary DataWeb and Big Data10.1007/978-981-97-7244-5_19(286-301)Online publication date: 31-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7244-5_19
Han SMa ZZhao WLv Z(2023)Neighbor-Discovery Recurrent Model for Point-of-Interest RecommendationProceedings of the 2023 6th International Conference on Big Data Technologies10.1145/3627377.3627386(52-59)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1145/3627377.3627386
Lv ZCheng ZLi JXu ZYang Z(2023)TreeCN: Time Series Prediction With the Tree Convolutional Network for Traffic PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332581725:5(3751-3766)Online publication date: 27-Oct-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3325817

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

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Published In

cover image Data & Knowledge Engineering

Data & Knowledge Engineering Volume 146, Issue C

Jul 2023

222 pages

ISSN:0169-023X

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 July 2023

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Cited By

Wang BHe XLin HShen GKong X(2024)An Epidemic Trend Prediction Model with Multi-source Auxiliary DataWeb and Big Data10.1007/978-981-97-7244-5_19(286-301)Online publication date: 31-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7244-5_19
Han SMa ZZhao WLv Z(2023)Neighbor-Discovery Recurrent Model for Point-of-Interest RecommendationProceedings of the 2023 6th International Conference on Big Data Technologies10.1145/3627377.3627386(52-59)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1145/3627377.3627386
Lv ZCheng ZLi JXu ZYang Z(2023)TreeCN: Time Series Prediction With the Tree Convolutional Network for Traffic PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332581725:5(3751-3766)Online publication date: 27-Oct-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3325817

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