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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References
Kraemer MU, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM, Du Plessis L, Faria NR, Li R, Hanage WP et al (2020) The effect of human mobility and control measures on the covid-19 epidemic in china. Science 368(6490):493–497
Bao H, Zhou X, Zhang Y, Li Y, Xie Y (2020) Covid-gan: estimating human mobility responses to covid-19 pandemic through spatio-temporal conditional generative adversarial networks. In: Proceedings of the 28th international conference on advances in geographic information systems. SIGSPATIAL ’20, pp 273–282. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3397536.3422261
Liang W, Liu Z, Liu C (2020) Dawson: A domain adaptive few shot generation framework. arXiv preprint arXiv:2001.00576
Yao H, Liu Y, Wei Y, Tang X, Li Z (2019) Learning from multiple cities: a meta-learning approach for spatial-temporal prediction. In: The World Wide Web conference, pp 2181–2191
Zhang Y, Li Y, Zhou X, Luo J (2020) cst-ml: Continuous spatial-temporal meta-learning for traffic dynamics prediction. In: 2020 IEEE international conference on data mining (ICDM), pp 1418–1423. IEEE
Finn C, Xu K, Levine S (2018) Probabilistic model-agnostic meta-learning. In: Advances in neural information processing systems, 31
Bao H, Zhou X, Xie Y, Li Y, Jia X (2022) Storm-gan: spatio-temporal meta-gan for cross-city estimation of human mobility responses to covid-19. In: 2022 IEEE international conference on data mining (ICDM), pp 1–10. IEEE
Gauthier J (2014) Conditional generative adversarial nets for convolutional face generation. Class Project for Stanford CS231N: convolutional neural networks for visual recognition. Winter semester 2014(5):2
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680
Kang Y, Gao S, Liang Y, Li M, Rao J, Kruse J (2020) Multiscale dynamic human mobility flow dataset in the us during the covid-19 epidemic. Sci data 7(1):1–13
Kullback S (1997) Information theory and statistics. Courier Corporation
Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: International conference on machine learning, pp 1725–1735
Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, pp 1126–1135
Antoniou A, Edwards H, Storkey A (2018) How to train your maml. arXiv preprint arXiv:1810.09502
Baydin AG, Cornish R, Rubio DM, Schmidt M, Wood F (2017) Online learning rate adaptation with hypergradient descent. arXiv preprint arXiv:1703.04782
Centers for Disease Control and Prevention. https://www.cdc.gov/ (2020)
American Community Survey (ACS). https://www.census.gov/programs-surveys/acs (2020)
SafeGraph. https://www.safegraph.com/ (2020)
Getis A (2008) A history of the concept of spatial autocorrelation: a geographer’s perspective. Geogr Anal 40(3):297–309
Hoerl RW (2020) Ridge regression: a historical context. Technometrics 62(4):420–425
Zhang Y, Li Y, Zhou X, Kong X, Luo J (2019) Trafficgan: off-deployment traffic estimation with traffic generative adversarial networks. In: 2019 IEEE international conference on data mining (ICDM), pp 1474–1479. IEEE
Soucy J-PR, Sturrock SL, Berry I, Daneman N, MacFadden DR, Brown KA (2020) Estimating the effect of physical distancing on the covid-19 pandemic using an urban mobility index. medRxiv
Bryant P, Elofsson A (2020) Estimating the impact of mobility patterns on covid-19 infection rates in 11 European countries. medRxiv
Gao S, Rao J, Kang Y, Liang Y, Kruse J (2020) Mapping county-level mobility pattern changes in the united states in response to covid-19. SIGSPATIAL Special 12(1):16–26
Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, AP y Piontti, Mu K, Rossi L, Sun K (2020) The effect of travel restrictions on the spread of the 2019 novel coronavirus (covid-19) outbreak. Science 368(6489):395–400
Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, Munday JD, Kucharski AJ, Edmunds WJ, Sun F et al (2020) Feasibility of controlling covid-19 outbreaks by isolation of cases and contacts. The Lancet Global Health
Cho H, Ippolito D, Yu YW (2020) Contact tracing mobile apps for covid-19: privacy considerations and related trade-offs. arXiv preprint arXiv:2003.11511
Chang S, Wilson ML, Lewis B, Mehrab Z, Dudakiya KK, Pierson E, Koh PW, Gerardin J, Redbird B, Grusky D, et al (2021) Supporting covid-19 policy response with large-scale mobility-based modeling. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining, pp 2632–2642
Jiang R, Song X, Fan Z, Xia T, Wang Z, Chen Q, Cai Z, Shibasaki R (2021) Transfer urban human mobility via poi embedding over multiple cities. ACM Trans Data Sci 2(1):1–26
Yuan Z, Zhou X, Yang T (2018) 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 and data mining, pp 984–992
Pan Z, Wang Z, Wang W, Yu Y, Zhang J, Zheng Y (2019) Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 2683–2691
Xie Y, Bao H, Shekhar S, Knight J (2018) A timber framework for mining urban tree inventories using remote sensing datasets. In: 2018 IEEE international conference on data mining (ICDM), pp 1344–1349. IEEE
Li Z, Xie Y, Jia X, Stuart K, Delaire C, Skakun S (2023) Point-to-Region Colearning for Poverty Mapping at High Resolution Using Satellite Imagery. Proceedings of the AAAI Conference on Artificial Intelligence. 37:14321–14328
Xie Y, Cai J, Bhojwani R, Shekhar S, Knight J (2020) A locally-constrained yolo framework for detecting small and densely-distributed building footprints. Int J Geogr Inf Sci 34(4):777–801
Zhang X, Li Y, Zhou X, Luo J (2019) Unveiling taxi drivers’ strategies via cgail: Conditional generative adversarial imitation learning. In: 2019 IEEE international conference on data mining (ICDM), pp 1480–1485. IEEE
Zhang C, Zhu F, Lv Y, Ye P, Wang F-Y (2021) Mlrnn: taxi demand prediction based on multi-level deep learning and regional heterogeneity analysis. IEEE Trans Intell Transport Syst
Xu S, Zhang R, Cheng W, Xu J (2020) Mtlm: a multi-task learning model for travel time estimation. GeoInformatica, 1–17
Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125–1134
Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690
Reed S, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H (2016) Generative adversarial text to image synthesis. arXiv preprint arXiv:1605.05396
Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4401–4410
Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul Smolley S (2017) Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2794–2802
Che T, Li Y, Jacob AP, Bengio Y, Li W (2016) Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136
Simonovsky M, Komodakis N (2018) Graphvae: towards generation of small graphs using variational autoencoders. In: International conference on artificial neural networks, pp 412–422. Springer
Wang H, Wang J, Wang J, Zhao M, Zhang W, Zhang F, Xie X, Guo M (2017) Graphgan: graph representation learning with generative adversarial nets. arXiv preprint arXiv:1711.08267
Nichol A, Schulman J (2018) Reptile: a scalable metalearning algorithm. arXiv preprint arXiv:1803.02999
Wang L, Geng X, Ma X, Liu F, Yang Q (2018) Cross-city transfer learning for deep spatio-temporal prediction. arXiv preprint arXiv:1802.00386
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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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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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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DOI: https://doi.org/10.1007/s10115-023-01921-7