NDLI: Particle filter for real-time human mobility prediction following unprecedented disaster

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Particle filter for real-time human mobility prediction following unprecedented disaster

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Particle filter for real-time human mobility prediction following unprecedented disaster

Content Provider	ACM Digital Library
Author	Kashiyama, Takehiro Sudo, Akihito Nakano, Shin'ya Sekimoto, Yoshihide Song, Xuan Higuchi, Tomoyuki Kanasugi, Hiroshi Yabe, Takahiro Saito, Masaya
Abstract	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.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450345897
DOI	10.1145/2996913.2997000
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Disaster management Gps data Bayesian inference Human mobility prediction
Content Type	Text
Resource Type	Article

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