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OD-HyperNet: A Data-Driven Hyper-Network Model for Origin-Destination Matrices Completion Using Partially Observed Data

In: Liss 2020

Author

Listed:
  • Yuxuan Xiu

    (Tsinghua University Shenzhen)

  • Wanda Li

    (Tsinghua University Shenzhen)

  • Jing Yang (Sunny) Xi

    (Tsinghua University Shenzhen)

  • Wai Kin (Victor) Chan

    (Tsinghua University Shenzhen)

Abstract

Estimating the inter-city population flow is critical for modeling the spread of COVID-19. However, for most cities, it is difficult to extract accurate population numbers for inflow and outflow. On the other hand, mobile carriers and Internet companies can estimate the distribution of population flow by tracking their users; but their data only cover part of the travelers. In this paper, we present a data-driven hyper-network model to aggregate these two types of data and complete the inter-city OD matrix. We first propose a cross-layer breadth-first traversal algorithm to estimate the inflow and outflow population of each city, then complete the OD matrix with an optimization model. Our experiments on a real-world dataset prove the accuracy and efficiency of our model.

Suggested Citation

  • Yuxuan Xiu & Wanda Li & Jing Yang (Sunny) Xi & Wai Kin (Victor) Chan, 2021. "OD-HyperNet: A Data-Driven Hyper-Network Model for Origin-Destination Matrices Completion Using Partially Observed Data," Springer Books, in: Shifeng Liu & Gábor Bohács & Xianliang Shi & Xiaopu Shang & Anqiang Huang (ed.), Liss 2020, pages 335-350, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-4359-7_24
    DOI: 10.1007/978-981-33-4359-7_24
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