JDR Vol.13 No.2 pp. 347-357
doi: 10.20965/jdr.2018.p0347


Hybrid System for Generating Data on Human Flow in a Tsunami Disaster

Takehiro Kashiyama*1,†, Yoshihide Sekimoto*1, Masao Kuwahara*2, Takuma Mitani*3, and Shunichi Koshimura*4

*1Institute of Industrial Science, The University of Tokyo
Komaba 4-6-1, Meguro-ku, Tokyo 153-8505, Japan

Corresponding author

*2Graduate School of Information Sciences, Tohoku University, Miyagi, Japan

*3Center for Spatial Information Science, The University of Tokyo, Chiba, Japan

*4International Research Institute of Disaster Sciences, Tohoku University, Miyagi, Japan

October 31, 2017
February 11, 2018
Online released:
March 19, 2018
March 20, 2018
evacuation behavior, damage prediction, traffic simulation, tsunami damage, migration data

Japan has suffered significant damage from countless earthquakes throughout its history. Thus, it is important to take prompt and effective measures against major future earthquakes predicted. Among the components of damage, measures against tsunamis are a top priority, as demonstrated by the catastrophic losses caused by the Great East Japan Earthquake (GEJE). To date, many studies have been conducted on tsunamis to investigate detection, prediction of flooding, and models of evacuation behaviors. Therefore, this study sought to integrate the results of increasingly advanced research in various fields to construct a system that generates data on human flow during a tsunami disaster. We proceeded with a scenario assuming the Great Nankai Trough Earthquake and considered Kochi City as a case study to conduct a trial tsunami evacuation simulation. We validated and evaluated the evacuation behaviors as a scheme for utilizing knowledge of the ever-changing conditions of evacuation and conducted a visualization and analysis of the simulation results.

Cite this article as:
T. Kashiyama, Y. Sekimoto, M. Kuwahara, T. Mitani, and S. Koshimura, “Hybrid System for Generating Data on Human Flow in a Tsunami Disaster,” J. Disaster Res., Vol.13 No.2, pp. 347-357, 2018.
Data files:
  1. [1] Fire and Disaster Management Agency (Japan), “155th Report about the Great East Japan Earthquake,” [accessed Nov. 1, 2017]
  2. [2] Cabinet Office, Goverment of Japan, “Survey of damage amount in Great East Japan Earthquake,” [accessed Nov. 1, 2017]
  3. [3] The Headquarters For Earthquake Research Promotion, “Long-term evaluation results list of active faults and subduction zone earthquakes announced so far,” [accessed Nov. 1, 2017]
  4. [4] K. Kawaguchi, S. kaneko, T. Nishida and T. Komine, “Construction of the DONET real-time seafloor observatory for earthquakes and tsunami monitoring, Seafloor Observatories,” P. Favali et al., Springer Praxis Books, pp. 211-228, 2015.
  5. [5] T. Inoue, Y. Ohta, S. Koshimura, R. Hino, S. Kawamoto, Y. Hiyama, and Y. Doke, “A study on methods for applying fault models rapidly estimated using read-time gnss to tsunami simulation,” Journal of Japan Society of Civil Engineers, Vol.72, No.2, 2016.
  6. [6] A. Musa, Matsuoka,Y. Murashima, S. Koshimura, R, Hino, Y. Ohta, and H. Kobayashi, “A real-time tsunami inundation forecast system for tsunami disaster prevention and mitigation,” SC15 Extended Abstract, 2015.
  7. [7] O. Osaragi, “Modeing of decision making and behavior for returning home after a devastating earthquake,” Journal of Architectural Institute of Japan, Vol.73, No.634, pp. 2679-2687, 2008.
  8. [8] U. Hiroi, N. Sekiya, R. Nakajima, S. Waragai, and H. Hanahara, “Questionnaire Survey concerning Stranded Commuters in Metropolitan Area in the East Japan Great Earthquake,” Journal of Institute of Social Safety Science, Vol.15, pp. 343-353, 2011.
  9. [9] K. Ito, S. Aono, and N. Ohmori, “Empirical study on stop-offs en route home in the aftermath of an earthquake disaster in the Tokyo metropolitan area,” Journal of the City Planning Institute of Japan, Vol.48, No.3, pp. 873-878, 2013.
  10. [10] T. Yabe, Y. Sekimoto, T. Kashiyama, and H. Kanasugi, “Making Real-Time Predictions of People’s Irregular Movement In a Metropolitan Scale Under Disaster Situations,” Proceedings of the 14th International Conference on CUPUM, 2015.
  11. [11] A. Sudo, T. Kashiyama, T. Yabe, H. Kanasugi, and Y. Sekimoto, “Earthquake Victim Distribution Estimation using Filtering Approach,” Journal of Disaster Research, 2016.
  12. [12] M. Kuwahara, T. Ohata, and Y. Oishi, “Normative behaviour-based first-best analysis for disaster evacuation,” Transportmetrica B, Transport Dynamics, 2017.
  13. [13] Noelia Caceres, Luis M. Romero, Francisco G. Benitez, and Jose M. del Castillo, “Traffic flow estimation models using cellular phone data,” IEEE Transactions on Intelligent Transportation Systems, Vol.13, No.3, 2012.
  14. [14] S. Koshimura, “Fusion of Real-time Disaster Simulation and Big Data Assimilation – Recent Progress –,” Journal of Disaster Research, Vol.12, No.2, pp. 226-232, 2017.
  15. [15] S. Koshimura, “Developing real-time tsunami inundation and damage forecasting technology,” Journal of Information Processing and Management, Japan Science and Technology Agency, Vol.59, No.12, 2017.
  16. [16] S. Koshimura, T. Oie, H. Yanagisawa, and F. Imamura, “Developing fragility functions for tsunami damage estimation using numerical model and post-tsunami data from Banda Aceh, Indonesia,” Coastal Engineering Journal, Vol.51, No.3, pp. 243-273, 2009.
  17. [17] Y. Sekimoto, A. Nishizawa, H. Yamada, R. Shibasaki, J. Kumagai, T. Sagara, Y. Kayama, and S. Ootomo, “Data mobilization by digital archiving of the Great East Japan Earthquake Survey,” Theory and applications of GIS, GIS Association of Japan, Vol.21, No.2, pp.1-9, 2013.
  18. [18] Digital Archiving of the Great East Japan Earthquake Survey, [accessed Nov. 1, 2017]
  19. [19] Cabinet Office, Goverment of Japan, “Survey on evacuation at earthquake tsunami in the Great East Japan Earthquake,” [accessed Nov. 1, 2017]
  20. [20] PFLOW, [accessed Nov. 1, 2017]
  21. [21] Y. Sekimoto, R. Shibasaki, H. Kanasugi, T. Usui, and Y. Shimazaki, “PFLOW: Reconstruction of people flow recycling large-scale social survey data,” IEEE Pervasive Computing, Vol.10, No.4, pp. 27-35, 2011.
  22. [22] Cabinet Office, “Goverment of Japan:Damage assumption items and methods of calculating them,” [accessed Nov. 1, 2017]
  23. [23] H. Okamura, M. Kuwahara, T. Yoshii, and I. Nishikawa, “Development and verification of general street network simulation models,” Proceedings of Japan Society of Traffic Engineers, No.16, pp. 93-96, 1996.
  24. [24] Y. Tsukada, T. Kiriyama, H. Hokuhara, and K. Hamaya, “Study on new road design method about traffic capacity,” Technical Note of National Institute for Land and Infrastructure Management, No.317, 2006.
  25. [25] Digital Road Map, [accessed Nov. 1, 2017]
  26. [26] S. Ueyama, Y. Akiyama, and R. Shibasaki, “Integrated Visual Exploration Tool for Fusion of Mass Movement and Static Data,” Proceedings of the 14th International Conference on CUPUM, 2015.
  27. [27] Mobmap, [accessed Nov. 1, 2017]

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024