Human Mobility Estimation Following Massive Disaster Using Filtering Approach

Akihito Sudo; Takehiro Kashiyama; Takahiro Yabe; Hiroshi Kanasugi; Yoshihide Sekimoto

doi:10.20965/jdr.2016.p0217

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JDR Vol.11 No.2 pp. 217-224

(2016)

doi: 10.20965/jdr.2016.p0217

Paper:

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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

Received:

October 13, 2015

Accepted:

February 6, 2016

Online released:

March 18, 2016

Published:

March 1, 2016

Keywords:

human mobility, location data, Bayesian inference, disaster management

Abstract

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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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti, “Estimating origin-destination flows using mobile phone location data,” IEEE Pervasive Computing, Vol.4, No.10, p. 3644, 2011.

[2] [2] A. Sevtsuk and C. Ratti, “Does urban mobility have a daily routine? learning from the aggregate data of mobile networks,” Journal of Urban Technology, Vol.17, No.1, p. 4160, 2010.

[3] [3] J. Reades, F. Calabrese, and C. Ratti, “Eigenplaces: analysing cities using the space-time structure of the mobile phone network,” Environment and Planning B: Planning and Design, Vol.36, No.5, pp. 824-836, 2009.

[4] [4] Marta C. Gonzalez, A. H. Cesar, and A. Barabasi, “Understanding individual human mobility patterns,” Nature, Vol.453, No.7196, pp. 779-782, 2008.

[5] [5] Adam J. Pel, C. J. Bliemer, and S. P. Hoogendoorn, “A review on travel behaviour modelling in dynamic traffic simulation models for evacuations,” Transportation, Vol.39, No.1, pp. 97-123, 2012.

[6] [6] G. Kitagawa, “Monte Carlo filter and smoother for non-Gaussian nonlinear state space models,” Journal of computational and graphical statistics, Vol.5, No.1, pp. 1-25, 1996.

[7] [7] P. Cheng, Z. Qiu, and B. Ran, “Particle filter based traffic state estimation using cell phone network data,” In Intelligent Transportation Systems Conference, ITSC’6, IEEE, pp. 1047-1052, IEEE, 2006.

[8] [8] K. Sasaki, K. Nakazawa, and T. Yamamoto, “Analysis of Traffic Volume Variation by Bayesian State-Space Model,” Japan Society of Traffic Engineers, Vol.47, No.2, pp. 27-32, 2012.

[9] [9] D. B. Work, O.-P. Tossavainen, Q. Jacobson, and A. M. Bayen, “Lagrangian sensing: traffic estimation with mobile devices,” In American Control Conference, ACC’9., pp. 1536-1543. IEEE, 2009.

[10] [10] D. B. Work, O.-P. Tossavainen, S. Blandin, A. M. Bayen, T. Iwuchukwu, and K. Tracton, “An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices,” Decision and Control, 2008. CDC 2008, 47^th IEEE, Conference on. IEEE, 2008.

[11] [11] T. Nakamura, Y. Sekimoto, T. Usui, and R. Shibasaki, “Estimation of People Flow in Urban Level using Particle Filter,” Japan Society of Civil Engineers, Vol.69, No.3, pp. 227-236, 2013.

[12] [12] M. G. Demissie, G. H. de Almeida Correia, and C. Bento, “Intelligent road traffic status detection system through cellular networks handover information: An ex- ploratory study,” Transportation Research Part C: Emerging Technologies, Vol.32(0), pp. 76-88, 2013.

[13] [13] M. S. Iqbal, C. F. Choudhury, P. Wang, and C. M. Gonzalez, “Development of origindestination matrices using mobile phone call data,” Transportation Research Part C: Emerging Technologies, Vol.40, pp. 63-74, 2014.

[14] [14] X. Song, Q. Zhang, Y. Sekimoto, and R. Shibasaki, “Prediction of human emergency behavior and their mobility following large-scale disaster,” In Proceedings of the 20^th ACM SIGKDD international conference on Knowledge discovery and datamining, p. 514. ACM, 2014.

[15] [15] L. Mihaylova and R. Boel, “A particle filter for freeway traffic estimation,” In Decision and Control, 2004. CDC. 43^rd, IEEE Conference on, Vol.2, pp. 2106-2111, IEEE, 2004.

[16] [16] Y. Sekimoto, R. Shibasaki, H. Kanasugi, T. Usui, and Y. Shimazaki, “Pflow: Reconstructing people flow recycling large-scale social survey data,” IEEE Pervasive Computing, Vol.10, No.4, pp. 002735, 2011.

[17] [17] H. Okamura, M. Kuwabara, and Y. Toshio, “Development and Evaluation of Simulation Model ‘SOUND’,” Proceedings of Anual Conference of JSTE, pp. 93-96, 1996.

[18] [18] X. Song, Q. Zhang, Y. Sekimoto, R. Shibasaki, N. J. Yuan, and X. Xie, “A simulator of human emergency mobility following disasters: Knowledge transfer from big disaster data,” In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.

[19] [19] X. Lu, L. Bengtsson, and P. Holme, “Predictability of population displacement after the 2010 haiti earthquake,” Proceedings of the National Academy of Sciences, Vol.109, No.29, pp. 11576-11581, 2012.

[20] [20] E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility: user movement in location-based social networks,” In Proceedings of the 17^th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1082-1090, ACM, 2011.

[21] [21] Y. Sekimoto, A. Nishizawa, and H. Yamada, “Data mobilization by digital archiving of the great east japan earthquake survey,” GIS-Theory and Application, Vol.21, No.2, pp. 87-95, 2013 (in Japanese).

Human Mobility Estimation Following Massive Disaster Using Filtering Approach

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

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