Skip to main content
SpringerLink
Log in

Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19

  • Conference paper
  • First Online:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12978))

Abstract

Modeling and predicting human mobility are of great significance to various application scenarios such as intelligent transportation system, crowd management, and disaster response. In particular, in a severe pandemic situation like COVID-19, human movements among different regions are taken as the most important point for understanding and forecasting the epidemic spread in a country. Thus, in this study, we collect big human GPS trajectory data covering the total 47 prefectures of Japan and model the daily human movements between each pair of prefectures with time-series Origin-Destination (OD) matrix. Then, given the historical observations from past days, we predict the countrywide OD matrices for the future one or more weeks by proposing a novel deep learning model called Origin-Destination Convolutional Recurrent Network (ODCRN). It integrates the recurrent and 2-dimensional graph convolutional components to deal with the highly complex spatiotemporal dependencies in sequential OD matrices. Experiment results over the entire COVID-19 period demonstrate the superiority of our proposed methodology over existing OD prediction models. Last, we apply the predicted countrywide OD matrices to the SEIR model, one of the most classic and widely used epidemic simulation model, to forecast the COVID-19 infection numbers for the entire Japan. The simulation results also demonstrate the high reliability and applicability of our countrywide OD prediction model for a pandemic scenario like COVID-19.

R. Jiang and Z. Wang—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    \(\beta \) here different with Definition 3 is a widely used notation for epidemic parameter.

References

  1. https://qianxi.baidu.com/

  2. https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology/The_SIR_model

  3. Bai, L., Yao, L., Kanhere, S., Wang, X., Sheng, Q., et al.: STG2Seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting. In: IJCAI, pp. 1981–1987 (2019)

    Google Scholar 

  4. Bertuzzo, E., et al.: The geography of COVID-19 spread in Italy and implications for the relaxation of confinement measures. Nat. Commun. 11(1), 1–11 (2020)

    Article  Google Scholar 

  5. Bruna, J., Zaremba, W., Szlam, A., Lecun, Y.: Spectral networks and locally connected networks on graphs. In: International Conference on Learning Representations (2014)

    Google Scholar 

  6. Chang, S., et al.: Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589(7840), 82–87 (2021)

    Article  Google Scholar 

  7. Chinazzi, M., et al.: The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368(6489), 395–400 (2020)

    Article  Google Scholar 

  8. Della Rossa, F., et al.: A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic. Nat. Commun. 11(1), 1–9 (2020)

    Article  MathSciNet  Google Scholar 

  9. Diao, Z., Wang, X., Zhang, D., Liu, Y., Xie, K., He, S.: Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 890–897 (2019)

    Google Scholar 

  10. Feng, J., et al.: DeepMove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 World Wide Web Conference, pp. 1459–1468. International World Wide Web Conferences Steering Committee (2018)

    Google Scholar 

  11. Gao, Q., Zhou, F., Trajcevski, G., Zhang, K., Zhong, T., Zhang, F.: Predicting human mobility via variational attention. In: The World Wide Web Conference, pp. 2750–2756. ACM (2019)

    Google Scholar 

  12. Gatto, M., et al.: Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures. Proc. Natl. Acad. Sci. 117(19), 10484–10491 (2020)

    Article  Google Scholar 

  13. Geng, X., et al.: Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: 2019 AAAI Conference on Artificial Intelligence (AAAI 2019) (2019)

    Google Scholar 

  14. Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929 (2019)

    Google Scholar 

  15. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024–1034 (2017)

    Google Scholar 

  16. Hoang, M.X., Zheng, Y., Singh, A.K.: FCCF: forecasting citywide crowd flows based on big data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10 (2016)

    Google Scholar 

  17. Hu, J., Yang, B., Guo, C., Jensen, C.S., Xiong, H.: Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1417–1428. IEEE (2020)

    Google Scholar 

  18. Jiang, R., et al.: Deep ROI-based modeling for urban human mobility prediction. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(1), 1–29 (2018)

    Google Scholar 

  19. Jiang, R., et al.: DeepUrbanMomentum: an online deep-learning system for short-term urban mobility prediction. In: AAAI, pp. 784–791 (2018)

    Google Scholar 

  20. Jiang, R., et al.: DeepUrbanEvent: a system for predicting citywide crowd dynamics at big events. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2114–2122. ACM (2019)

    Google Scholar 

  21. Kraemer, M.U., et al.: The effect of human mobility and control measures on the COVID-19 epidemic in china. Science 368(6490), 493–497 (2020)

    Article  Google Scholar 

  22. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  23. Li, R., et al.: Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 368(6490), 489–493 (2020)

    Article  Google Scholar 

  24. Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International Conference on Learning Representations (2018)

    Google Scholar 

  25. Lin, Z., Feng, J., Lu, Z., Li, Y., Jin, D.: DeepSTN+: context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1020–1027 (2019)

    Google Scholar 

  26. Liu, L., Qiu, Z., Li, G., Wang, Q., Ouyang, W., Lin, L.: Contextualized spatial-temporal network for taxi origin-destination demand prediction. IEEE Trans. Intell. Transp. Syst. 20(10), 3875–3887 (2019)

    Article  Google Scholar 

  27. Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)

    Google Scholar 

  28. Monti, F., Bronstein, M., Bresson, X.: Geometric matrix completion with recurrent multi-graph neural networks. In: Advances in Neural Information Processing Systems, pp. 3697–3707 (2017)

    Google Scholar 

  29. Shi, H., et al.: Predicting origin-destination flow via multi-perspective graph convolutional network. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1818–1821. IEEE (2020)

    Google Scholar 

  30. Sun, J., Zhang, J., Li, Q., Yi, X., Liang, Y., Zheng, Y.: Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. IEEE Trans. Knowl. Data Eng. (2020)

    Google Scholar 

  31. Wang, D., Cao, W., Li, J., Ye, J.: DeepSD: supply-demand prediction for online car-hailing services using deep neural networks. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 243–254. IEEE (2017)

    Google Scholar 

  32. Wang, Y., Yin, H., Chen, H., Wo, T., Xu, J., Zheng, K.: Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1227–1235 (2019)

    Google Scholar 

  33. Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. In: IJCAI, pp. 1907–1913 (2019)

    Google Scholar 

  34. Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)

    Google Scholar 

  35. Yao, H., et al.: Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  36. Ye, J., Sun, L., Du, B., Fu, Y., Tong, X., Xiong, H.: Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 305–313 (2019)

    Google Scholar 

  37. Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 3634–3640. AAAI Press (2018)

    Google Scholar 

  38. Yuan, Z., Zhou, X., Yang, T.: Hetero-ConvLSTM: a deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 984–992. ACM (2018)

    Google Scholar 

  39. Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

    Google Scholar 

  40. Zhang, J., Zheng, Y., Sun, J., Qi, D.: Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Trans. Knowl. Data Eng. 32(3), 468–478 (2019)

    Article  Google Scholar 

  41. Zhang, Q., Chang, J., Meng, G., Xiang, S., Pan, C.: Spatio-temporal graph structure learning for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1177–1185 (2020)

    Google Scholar 

  42. Zonoozi, A., Kim, J.J., Li, X.L., Cong, G.: Periodic-CRN: a convolutional recurrent model for crowd density prediction with recurring periodic patterns. In: IJCAI, pp. 3732–3738 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Renhe Jiang, Zhaonan Wang, Zekun Cai, Chuang Yang, Zipei Fan, Xuan Song & Ryosuke Shibasaki

  2. LocationMind Inc., Tokyo, Japan

    Tianqi Xia & Go Matsubara

  3. BlogWatcher Inc., Tokyo, Japan

    Hiroto Mizuseki

  4. Southern University of Science and Technology, Shenzhen, China

    Renhe Jiang, Zipei Fan & Xuan Song

Authors
  1. Renhe Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaonan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zekun Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chuang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zipei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tianqi Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Go Matsubara
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hiroto Mizuseki
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xuan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ryosuke Shibasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuan Song .

Editor information

Editors and Affiliations

  1. Facebook AI, Seattle, WA, USA

    Yuxiao Dong

  2. Torre Telefonica, Barcelona, Spain

    Nicolas Kourtellis

  3. Bielefeld University, CITEC, Bielefeld, Germany

    Barbara Hammer

  4. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiang, R. et al. (2021). Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12978. Springer, Cham. https://doi.org/10.1007/978-3-030-86514-6_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86514-6_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86513-9

  • Online ISBN: 978-3-030-86514-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation