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.

References

[1]

Alemdar K.D., Ö Kaya., Canale A., et al., Evaluation of air quality index by spatial analysis depending on vehicle traffic during the COVID-19 outbreak in Turkey, Energies 14 (18) (2021) 5729,.

[2]

Saurabh S., Madria S., Mondal A., et al., An analytical model for information gathering and propagation in social networks using random graphs, Data Knowl. Eng. 129 (2020),.

[3]

Lv Z., Li J., Dong C., et al., Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index, Data Knowl. Eng. 135 (2021),.

Digital Library

[4]

Lu J., Lin A., Jiang C., et al., Influence of transportation network on transmission heterogeneity of COVID-19 in China, Transp. Res. C 129 (2021),.

[5]

Zhao P., Gao Y., Public transit travel choice in the post COVID-19 pandemic era: An application of the extended theory of planned behavior, Travel Behav. Soc. 28 (2022) 181–195,.

[6]

Zhang Y., Yang L., Wang X., Analysis and calculating of comprehensive urban vitality index by multi-source temporal-spatial big data and EW-TOPSIS, in: 2021 IEEE International Conference on Data Science and Computer Application, IEEE, 2021, pp. 196–201,.

[7]

Chen J., Chen Y., The study on the influence of highway transportation on regional economic development, in: 2017 5th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference, Atlantis Press, 2018, pp. 360–364,.

[8]

Kurth M., Kozlowski W., Ganin A., et al., Lack of resilience in transportation networks: Economic implications, Transp. Res. D 86 (2020),.

[9]

Li Y., Yang Y., Luo G., et al., The economic recovery from traffic restriction policies during the COVID-19 through the perspective of regional differences and sustainable development: Based on human mobility data in China, Sustainability 14 (11) (2022) 6453,.

[10]

Sharifi A., Khavarian-Garmsir A.R., Kummitha R.K.R., Contributions of smart city solutions and technologies to resilience against the COVID-19 pandemic: A literature review, Sustainability 13 (14) (2021) 8018,.

[11]

Abdi A., Amrit C., A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities, PeerJ Comput. Sci. 7 (2021),.

[12]

Xu H., Jiang C., Deep belief network-based support vector regression method for traffic flow forecasting, Neural Comput. Appl. 32 (7) (2020) 2027–2036,.

[13]

Cai L., Yu Y., Zhang S., et al., A sample-rebalanced outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting, IEEE Access 8 (2020) 22686–22696,.

[14]

Han L., Huang Y.S., Short-term traffic flow prediction of road network based on deep learning, IET Intell. Transp. Syst. 14 (6) (2020) 495–503,.

[15]

Zhang Y., Short-term traffic flow prediction methods: A survey, J. Phys. Conf. Ser. 1486 (5) (2020),. IOP Publishing.

[16]

Guo K., Hu Y., Qian Z., et al., Optimized graph convolution recurrent neural network for traffic prediction, IEEE Trans. Intell. Transp. Syst. 22 (2) (2020) 1138–1149,.

[17]

Wu S., Spatiotemporal dynamic forecasting and analysis of regional traffic flow in urban road networks using deep learning convolutional neural network, IEEE Trans. Intell. Transp. Syst. 23 (2) (2021) 1607–1615,.

Digital Library

[18]

Montanino M., Monteil J., Punzo V., From homogeneous to heterogeneous traffic flows: Lp string stability under uncertain model parameters, Transp. Res. B 146 (2021) 136–154,.

[19]

Yuan H., Li G., A survey of traffic prediction: from spatio-temporal data to intelligent transportation, Data Sci. Eng. 6 (1) (2021) 63–85,.

[20]

Li W., Wang X., Zhang Y., et al., Traffic flow prediction over muti-sensor data correlation with graph convolution network, Neurocomputing 427 (2021) 50–63,.

[21]

Y. Zhao, J. Qi, Q. Liu, et al., Wgcn: graph convolutional networks with weighted structural features, in: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, pp. 624–633, https://doi.org/10.1145/3404835.3462834.

[22]

Huang Y., Song X., Zhang S., et al., Transfer learning in traffic prediction with graph neural networks, in: 2021 IEEE International Intelligent Transportation Systems Conference, IEEE, 2021, pp. 3732–3737,.

Digital Library

[23]

Peng H., Du B., Liu M., et al., Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning, Inform. Sci. 578 (2021) 401–416,.

Digital Library

[24]

Pang Y., Kashiyama T., Sekimoto Y., Development of a reinforcement learning based agent model and people flow data to Mega Metropolitan Area, in: 2021 IEEE International Conference on Big Data, IEEE, 2021, pp. 3755–3759,.

[25]

Chen Y., Kang Y., Chen Y., et al., Probabilistic forecasting with temporal convolutional neural network, Neurocomputing 399 (2020) 491–501,.

[26]

Zhao Z., Li Q., Zhang Z., et al., Combining a parallel 2D CNN with a self-attention dilated residual network for CTC-based discrete speech emotion recognition, Neural Netw. 141 (2021) 52–60,.

Digital Library

[27]

Wang Y., Lv Z., Sheng Z., et al., A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic, Adv. Eng. Inform. 53 (2022),.

Digital Library

[28]

Z. Zhang, M. Liu, W. Xu, Spatial-Temporal Multi-Head Attention Networks for Traffic Flow Forecasting, in: The 5th International Conference on Computer Science and Application Engineering, 2021, pp. 1–7, https://doi.org/10.1145/3487075.3487102.

[29]

Bui K.H.N., Cho J., Yi H., Spatial–temporal graph neural network for traffic forecasting: An overview and open research issues, Appl. Intell. (2021) 1–12,.

Digital Library

[30]

Zhang Y., Cheng T., Ren Y., et al., A novel residual graph convolution deep learning model for short-term network-based traffic forecasting, Int. J. Geogr. Inf. Sci. 34 (5) (2020) 969–995,.

[31]

Sun B., Zhao D., Shi X., et al., Modeling global spatial–temporal graph attention network for traffic prediction, IEEE Access 9 (2021) 8581–8594,.

[32]

Hu N., Zhang D., Xie K., et al., Graph learning-based spatial–temporal graph convolutional neural networks for traffic forecasting, Connect. Sci. 34 (1) (2022) 429–448,.

[33]

Fang W., Chen Y., Xue Q., Survey on research of RNN-based spatio-temporal sequence prediction algorithms, J. Big Data 3 (3) (2021) 97,.

[34]

Zhao L., Song Y., Zhang C., et al., T-gcn: A temporal graph convolutional network for traffic prediction, IEEE Trans. Intell. Transp. Syst. 21 (9) (2019) 3848–3858,.

[35]

Yu B., Yin H., Zhu Z., Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting, 2017, pp. 1–7,. arXiv preprint arXiv:1709.04875.

[36]

S. Guo, Y. Lin, N. Feng, et al., Attention based spatial–temporal graph convolutional networks for traffic flow forecasting, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 922–929, https://doi.org/10.1609/aaai.v33i01.3301922, (01).

[37]

Chen C., Cai F., Hu X., et al., HHGN: A hierarchical reasoning-based heterogeneous graph neural network for fact verification, Inf. Process. Manage. 58 (5) (2021),.

Digital Library

[38]

Tang J., Liang J., Liu F., et al., Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network, Transp. Res. C 124 (2021),.

[39]

Chen T., Zhang Y., Qian X., et al., A knowledge graph-based method for epidemic contact tracing in public transportation, Transp. Res. C 137 (2022),.

[40]

Qiao S., Han N., Huang J., et al., A dynamic convolutional neural network based shared-bike demand forecasting model, ACM Trans. Intell. Syst. Technol. 12 (6) (2021) 1–24,.

Digital Library

[41]

Wu B., Liang B., Zhang X., Turn tree into graph: Automatic code review via simplified ast driven graph convolutional network, Knowl.-Based Syst. 252 (2022),.

Digital Library

[42]

Bhavsar S.A., Patil V.H., Patil A.H., Graph partitioning and visualization in graph mining: a survey, Multimedia Tools Appl. (2022) 1–42,.

Digital Library

[43]

Khan S., Nazir S., García-Magariño I., et al., Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion, Comput. Electr. Eng. 89 (2021),.

[44]

Duan Z., Yang Y., Zhou W., Multi-view spatial-temporal adaptive graph convolutional networks for traffic forecasting, in: 2021 16th International Conference on Intelligent Systems and Knowledge Engineerin, IEEE, 2021, pp. 35–41,.

[45]

Zhang K., He F., Zhang Z., et al., Graph attention temporal convolutional network for traffic speed forecasting on road networks, Transp. B 9 (1) (2021) 153–171,.

[46]

Li J., Lu Y., Xu Z., et al., MILP: A memory improved LSTM prediction algorithm for gradient transmission time in distributed deep learning, in: ICC 2022-IEEE International Conference on Communications, IEEE, 2022, pp. 4462–4467,.

[47]

Hochreiter S., Schmidhuber J., Long short-term memory, Neural Comput. 9 (8) (1997) 1735–1780,.

Digital Library

[48]

Cho K., Merriënboer B.Van., Gulcehre C., et al., Learning phrase representations using RNN encoder–decoder for statistical machine translation, 2014, pp. 1–15,. arXiv preprint arXiv:1406.1078.

[49]

Defferrard M., Bresson X., Vandergheynst P., Convolutional neural networks on graphs with fast localized spectral filtering, Adv. Neural Inf. Process. Syst. 29 (2016) 1–9,.

[50]

Chen W., Yeo C.K., Lau C.T., et al., Leveraging social media news to predict stock index movement using RNN-boost, Data Knowl. Eng. 118 (2018) 14–24,.

[51]

Galić Z., Baranović M., Križanović K., et al., Geospatial data streams: Formal framework and implementation, Data Knowl. Eng. 91 (2014) 1–16,.

[52]

Papanikolaou Y., Tsoumakas G., Katakis I., Hierarchical partitioning of the output space in multi-label data, Data Knowl. Eng. 116 (2018) 42–60,.

Digital Library

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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

Issue’s Table of Contents

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

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https://dl.acm.org/doi/10.1007/978-981-97-7244-5_19
Yin SLi YWang JKong Y(2024)Graph Transformer Hawkes Processes for Causal Structure Learning in Telecom NetworksWireless Artificial Intelligent Computing Systems and Applications10.1007/978-3-031-71464-1_8(93-101)Online publication date: 21-Jun-2024
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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
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