Open AccessHuman Mobility Estimation Following Massive Disaster Using Filtering Approach
Author(s) -
Akihito Sudo,
Takehiro Kashiyama,
Takahiro Yabe,
Hiroshi Kanasugi,
Yoshihide Sekimoto
Publication year - 2016
Publication title -
journal of disaster research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.332
H-Index - 18
eISSN - 1883-8030
pISSN - 1881-2473
DOI - 10.20965/jdr.2016.p0217
Subject(s) - metropolitan area , estimation , data assimilation , computer science , population , particle filter , geography , environmental science , remote sensing , meteorology , filter (signal processing) , engineering , computer vision , systems engineering , demography , archaeology , sociology
Real-time estimation of people distribution immediately after a disaster is directly related to disaster reduction and is also highly beneficial in society. Recently, traffic estimation research has been actively performed using data assimilation techniques for observation data obtained from mobile phones. However, there has been no research on data assimilation technique using real-time gridded aggregated observation data obtained from mobile phones, which are available and can be used to estimate population flow and distribution in a metropolitan area during a large-scale disaster. In this research, population distribution in an urban area during a disaster was estimated using gridded aggregated observation data obtained from mobile phones, using particle filter. The experimental results indicated that the particle filters enabled high-precision real-time estimation in the Kanto district.