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Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

Authors:

Ryosuke Shibasaki,

Shu-Ching Chen,

Mei-Ling ShyuAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 2655 - 2665

https://doi.org/10.1145/3543507.3583991

Published: 30 April 2023 Publication History

Abstract

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level.

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Cited By

Deng JJiang RZhang JSong XLarson K(2024)Multi-modality spatio-temporal forecasting via self-supervised learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/223(2018-2026)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/223
Li JWang HChen XBaeza-Yates RBonchi F(2024)Physics-informed Neural ODE for Post-disaster Mobility RecoveryProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672027(1587-1598)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672027
Dong ZJiang RGao HLiu HDeng JWen QSong XBaeza-Yates RBonchi F(2024)Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series ForecastingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671961(631-641)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671961
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Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

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cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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Published: 30 April 2023

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

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Cited By

Deng JJiang RZhang JSong XLarson K(2024)Multi-modality spatio-temporal forecasting via self-supervised learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/223(2018-2026)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/223
Li JWang HChen XBaeza-Yates RBonchi F(2024)Physics-informed Neural ODE for Post-disaster Mobility RecoveryProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672027(1587-1598)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672027
Dong ZJiang RGao HLiu HDeng JWen QSong XBaeza-Yates RBonchi F(2024)Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series ForecastingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671961(631-641)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671961
Liu XHu JLi YDiao SLiang YHooi BZimmermann RChua TNgo CKa-Wei Lee RKumar RLauw H(2024)UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series ForecastingProceedings of the ACM Web Conference 202410.1145/3589334.3645434(4095-4106)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645434
Anno STsubouchi KShimosaka M(2024)Forecasting Lifespan of Crowded Events With Acoustic Synthesis-Inspired Segmental Long Short-Term MemoryIEEE Access10.1109/ACCESS.2024.341750912(87309-87322)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3417509
Deng JDeng JJiang RSong XElkind E(2023)Learning Gaussian mixture representations for tensor time series forecastingProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/231(2077-2085)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/231
Cai ZJiang RYang XWang ZGuo DKobayashi HSong XShibasaki RFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)MemDA: Forecasting Urban Time Series with Memory-based Drift AdaptationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614962(193-202)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614962

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