Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster
Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster
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DOI：
10.1145/3543507.3583991
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Renhe Jiang;Zhaonan Wang;Yudong Tao;Chuang Yang;Xuan Song;R. Shibasaki;Shu‐Ching Chen;Mei-Ling Shyu-Mei-L
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文献类型：
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作者：
Renhe Jiang;Zhaonan Wang;Yudong Tao;Chuang Yang;Xuan Song;R. Shibasaki;Shu‐Ching Chen;Mei-Ling Shyu-Mei-L
人员流动临近预报是智能交通规划、灾害响应和管理等领域的基础性研究问题，特别是飓风、流行病等重大灾害下的人员流动，会在很大程度上偏离日常生活，这使得任务更具挑战性。现有的工作主要集中在正常情况下的交通或人群流量预测。为了解决这个问题，在这项研究中，灾害相关的Twitter数据作为协变量，以了解公众对灾害事件的认识和关注，从而感知他们对人类流动的影响。因此，我们提出了一个元知识可解释的时空网络（Memeidae），它利用记忆网络和元学习融合社会媒体和人类移动数据。针对日本2019年台风季、日本2020年COVID-19大流行及美国2019年飓风季等三个真实世界灾害进行了广泛实验，以说明我们提出的解决方案的有效性。与最先进的时空深度模型和多变量时间序列深度模型相比，我们的模型可以实现上级性能的临近预报在国家和州一级的灾害情况下的人员流动。
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.