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Prediction of Urban Population-Facilities Interactions with Graph Neural Network

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Computational Science and Its Applications – ICCSA 2023 (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13956 ))

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Abstract

The urban population interacts with service facilities on a daily basis. The information on population-facilities interactions is considered when analyzing the current city organization and revealing gaps in infrastructure at the neighborhood level. However, often this information is limited to several observation areas. The paper presents a new graph-based deep learning approach to reconstruct population-facilities interactions. In the proposed approach, graph attention neural networks learn latent nodes’ representation and discover interpretable dependencies in a graph of interactions based on observed data of one part of the city. A novel normalization technique is used to balance doubly-constrained flows between two locations. The experiments show that the proposed approach outperforms classic models in a bipartite graph of population-facilities interactions.

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Notes

  1. 1.

    In Russian regions, social facilities, such as kindergartens, are usually built on standard projects determining maximum capacity.

  2. 2.

    https://epivec.github.io/TDLM/.

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Acknowledgments

This research is financially supported by the Russian Science Foundation, Agreement 17-71-30029 (https://rscf.ru/en/project/17-71-30029/), with co-financing of Bank Saint-Petersburg.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Kotlarska 2, Brno, 61137, Czech Republic

    Margarita Mishina, Stanislav Sobolevsky, Alexander Khrulkov & Alexander Belyi

  2. ITMO University, Birzhevaya line, 14, Saint-Petersburg, Russia

    Margarita Mishina, Alexander Khrulkov & Sergey Mityagin

  3. Center for Urban Science and Progress, New York University, 370 Jay Street, Brooklyn, 11201, NY, USA

    Stanislav Sobolevsky

  4. Institute of Law and Technology, Faculty of Law, Masaryk University, Veveri 70, Brno, 61180, Czech Republic

    Stanislav Sobolevsky

  5. Artificial Intelligence Research Institute (AIRI), Moscow, Russia

    Semen Budennyy

  6. Sber AI Lab, Moscow, Russia

    Elizaveta Kovtun & Semen Budennyy

Authors
  1. Margarita Mishina
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  2. Stanislav Sobolevsky
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  3. Elizaveta Kovtun
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  4. Alexander Khrulkov
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  5. Alexander Belyi
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  6. Semen Budennyy
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  7. Sergey Mityagin
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Corresponding author

Correspondence to Margarita Mishina .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Monash University, Clayton, VIC, Australia

    David Taniar

  4. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  5. University of Minho, Braga, Portugal

    Ana Cristina Braga

  6. University of Cagliari, Cagliari, Italy

    Chiara Garau

  7. National Technical University of Athens, Athens, Greece

    Anastasia Stratigea

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Mishina, M. et al. (2023). Prediction of Urban Population-Facilities Interactions with Graph Neural Network. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_23

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  • DOI: https://doi.org/10.1007/978-3-031-36805-9_23

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  • Online ISBN: 978-3-031-36805-9

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