JDR Vol.11 No.2 pp. 217-224
doi: 10.20965/jdr.2016.p0217


Human Mobility Estimation Following Massive Disaster Using Filtering Approach

Akihito Sudo*, Takehiro Kashiyama*, Takahiro Yabe**, Hiroshi Kanasugi***, and Yoshihide Sekimoto*

*Institute of Industrial Sience, the University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo, Japan

**Department of Civil Engineering, the University of Tokyo, Tokyo, Japan

***Earth Observation Data Integration and Fusion Research Initiative, the University of Tokyo, Tokyo, Japan

October 13, 2015
February 6, 2016
Online released:
March 18, 2016
March 1, 2016
human mobility, location data, Bayesian inference, disaster management
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.
Cite this article as:
A. Sudo, T. Kashiyama, T. Yabe, H. Kanasugi, and Y. Sekimoto, “Human Mobility Estimation Following Massive Disaster Using Filtering Approach,” J. Disaster Res., Vol.11 No.2, pp. 217-224, 2016.
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