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International Conference on Data Mining and Big Data

DMBD 2022: Data Mining and Big Data pp 77–92Cite as

Flow Prediction via Multi-view Spatial-Temporal Graph Neural Network

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1744))

Abstract

In recent years, the problem of traffic flow prediction in the urban environment has been widely concerned. However, the traffic flow prediction has not been effectively solved for the next period between the origin-destination region pair. In addition, multiple spatial-temporal traffic dependencies exist between the origin-destination area pairs. In this paper, three types of traffic dependencies between origin-destination region pairs were considered: the same origin dependency, same destination dependency, and transfer to dependency. This paper proposed a spatial-temporal forecasting framework for traffic flow prediction between pairs of urban regions with multi-view graphs. This work mainly considered the construction of spatial-temporal deep learning networks under three kinds of multi-view graphs. Finally, the prediction results under the three dependence relationships are fused to get the final prediction results. Comprehensive experiments on two datasets showed that the proposed framework has very high prediction performance, and outperforms the baseline model by more than 6%.

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Notes

  1. 1.

    https://www1.nyc.gov/site/tlc/about/tlc-triprecord-data.

  2. 2.

    https://www.capitalbikeshare.com/system-data.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 72201275.

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Authors and Affiliations

  1. Advanced Institute of Big Data, Beijing, China

    Shan Jiang, Qiang Wang, Chengjun Wang, Kun Liu & Shiqi Ning

  2. Institute of Software Chinese Academy of Science, Beijing, China

    Xinrun Xu

  3. University of Chines Academy of Sciences, Beijing, China

    Xinrun Xu

Authors
  1. Shan Jiang
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  2. Qiang Wang
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  3. Chengjun Wang
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  4. Kun Liu
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  5. Shiqi Ning
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  6. Xinrun Xu
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Corresponding author

Correspondence to Kun Liu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

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Jiang, S., Wang, Q., Wang, C., Liu, K., Ning, S., Xu, X. (2022). Flow Prediction via Multi-view Spatial-Temporal Graph Neural Network. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1744. Springer, Singapore. https://doi.org/10.1007/978-981-19-9297-1_7

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  • DOI: https://doi.org/10.1007/978-981-19-9297-1_7

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