Particle filter for real-time human mobility prediction following unprecedented disaster
Particle filter for real-time human mobility prediction following unprecedented disaster
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DOI：
10.1145/2996913.2997000
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Akihito Sudo;Takehiro Kashiyama;T. Yabe;H. Kanasugi;Xuan Song;T. Higuchi;S. Nakano;Masaya M. Saito;Y. Sekimoto
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文献类型：
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作者：
Akihito Sudo;Takehiro Kashiyama;T. Yabe;H. Kanasugi;Xuan Song;T. Higuchi;S. Nakano;Masaya M. Saito;Y. Sekimoto
大规模灾害发生后对人员流动性的实时估计将在救灾中发挥关键作用。由于大规模灾害中的人员流动性与他们通常的流动性有很大不同，因此需要实时的人员位置数据进行精确估计。由于隐私问题，实时数据是匿名的，一种流行的匿名形式是人口分布。在本文中，我们的目标是估计人口流动性后，前所未有的灾难，使用这样的人口分布数据。为了克服高维数等技术障碍，我们提出了一种新的粒子滤波器，通过设计建议分布。我们的建议分布提供了考虑预测模型和获得的观察的状态。因此，颗粒保持高可能性。在实验中，我们的方法实现了更准确的估计比基线，其估计的流动性是一致的调查研究。对于实时操作来说，计算成本非常低。使用东日本大地震当天收集的GPS数据进行评估。
Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.