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STORM-GAN+: spatio-temporal meta-GAN for cross-city estimation of heterogeneous human mobility responses to COVID-19

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Abstract

Estimating human mobility is essential during the COVID-19 pandemic because it provides policymakers with important information for non-pharmaceutical actions. Deep learning methods perform better on tasks with enough training data than traditional estimating techniques. However, estimating human mobility during the rapidly developing pandemic is challenging because of data non-stationarity, a lack of observations, and complicated social situations. Prior studies on estimating mobility either concentrate on a single city or cannot represent the spatio-temporal relationships across cities and time periods. To address these issues, we solve the cross-city human mobility estimation problem using a deep meta-generative framework. Recently, we proposed the Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model, which estimates dynamic human mobility responses under social and policy conditions relevant to COVID-19 and is facilitated by a novel spatio-temporal task-based graph (STTG) embedding. Although STORM-GAN achieves a good average estimation accuracy, it creates higher errors and exhibits over-fitting in particular cities due to spatial heterogeneity. To address these issues, in this paper, we extend our prior work by introducing an improved spatio-temporal deep generative model, namely STORM-GAN+. STORM-GAN+ deals with the difficulties by including a distance-based weighted training technique into the STTG embedding component to better represent the variety of knowledge transfer across cities. Furthermore, to mitigate the issue of overfitting, we modify the meta-learning training objective to teach estimated mobility. Finally, we propose a conditional meta-learning algorithm that explicitly tailors transferable knowledge to various task clusters. We perform comprehensive evaluations, and STORM-GAN+ approximates real-world human mobility responses more accurately than previous methods, including STORM-GAN.

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Acknowledgements

This paper is funded in part by Safety Research using Simulation University Transportation Center (SAFER-SIM). SAFER-SIM is funded by a grant from the U.S. Department of Transportation’s University Transportation Centers Program (69A3551747131). However, the U.S. Government assumes no liability for the contents or use thereof. Yiqun Xie is supported in part by NSF Grants 2105133, 2126474, 2147195, Google’s AI for Social Good Impact Scholars program, and the DRI award at the University of Maryland; and Xiaowei Jia is supported in part by NSF award 2147195, USGS award G21AC10207, Pitt Momentum Funds award, and CRC at the University of Pittsburgh. Yanhua Li was supported in part by NSF Grants IIS-1942680 (CAREER), CNS-1952085, CMMI-1831140, and DGE-2021871.

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Authors and Affiliations

  1. University of Iowa, Iowa City, IA, USA

    Han Bao & Xun Zhou

  2. University of Maryland, College Park, MD, USA

    Yiqun Xie

  3. Worcester Polytechnic Institute, Worcester, MA, USA

    Yanhua Li

  4. University of Pittsburgh, Pittsburgh, PA, USA

    Xiaowei Jia

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  1. Han Bao
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  2. Xun Zhou
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  3. Yiqun Xie
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  4. Yanhua Li
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Contributions

Han Bao wrote the main manuscript, implemented the algorithm, and conducted experiments. Han Bao, Xun Zhou, Yiqun Xie, Yanhua Li, and Xiaowei Jia contributed to the development of the proposed method. Xun Zhou, Yiqun Xie, Yanhua Li, and Xiaowei Jia reviewed and improved the article.

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Correspondence to Xun Zhou.

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Bao, H., Zhou, X., Xie, Y. et al. STORM-GAN+: spatio-temporal meta-GAN for cross-city estimation of heterogeneous human mobility responses to COVID-19. Knowl Inf Syst 65, 4759–4795 (2023). https://doi.org/10.1007/s10115-023-01921-7

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