single-dr.php

JDR Vol.10 No.6 pp. 1081-1090
(2015)
doi: 10.20965/jdr.2015.p1081

Paper:

A Distributed Autonomous Approach to Developing a Disaster Evacuation Assist System

Yasuki Iizuka*, Katsuya Kinoshita**, and Kayo Iizuka***

*Department of Mathematical Sciences, Tokai University
4-1-1 Kitakaname, Hiratsuka, Kanagawa 259-1292, Japan

**System Research Co., LTD, Aichi, Japan

***Senshu University, Kanagawa, Japan

Received:
July 31, 2015
Accepted:
October 21, 2015
Published:
December 1, 2015
Keywords:
disaster evacuation, distributed autonomous system, DCOP, multi-agent simulation
Abstract

In times of disaster, or other emergency situations, it is essential for people to be evacuated in a smooth manner. Evacuation must be performed promptly and safely. It is necessary to avoid generating a secondary disaster at the time of evacuation. However, this is not easy to realize, because people often tend to panic when faced with disaster, crowding the evacuation passageways of buildings. On the other hand, people do not attempt to evacuate themselves from danger when the normalcy bias has occurred. Therefore, evacuation guidance is very important. However, it is impossible to guide all evacuees through authorities such as disaster countermeasure offices. To deal with this issue, the authors propose a system that provides optimal evacuation guidance autonomously without central server. The system works on the mobile devices of evacuees, performs distributed calculations using the framework of the distributed constraint optimization problem on ad-hoc communication, and does not need a central server. In the experiment using multi-agent simulation, for the case where the evacuees can receive evacuation guidance from this system, the evacuation completion time decreased. This paper presents an overview and the evaluation results of the prototype of the disaster evacuation assistance system.

Cite this article as:
Y. Iizuka, K. Kinoshita, and K. Iizuka, “A Distributed Autonomous Approach to Developing a Disaster Evacuation Assist System,” J. Disaster Res., Vol.10, No.6, pp. 1081-1090, 2015.
Data files:
References
  1. [1]  Cabinet Office, “Disaster Management In Japan,” Government of Japan, 2011, online available at: http://www.bousai.go.jp/1info/pdf/saigaipanf_e.pdf [accessed July 30, 2015]
  2. [2]  A. R. Leite, F. Enembreck, and J.-P. A. Barthes, “Distributed constraint optimization problems: Review and perspectives,” Expert Systems with Applications, Vol.41, No.11, pp. 5139–5157, 2014.
  3. [3]  K. Iizuka, Y. Iizuka, and K. Yoshida, “A real-time disaster situation mapping system for university campuses,” in Online Communities and Social Computing, ser. Lecture Notes in Computer Science, A. Ozok and P. Zaphiris (Eds.), Springer Berlin / Heidelberg, 2011, Vol.6778, pp. 40–49.
  4. [4]  A. Fujihara and H. Miwa, “Effect of traffic volume in real-time disaster evacuation guidance using opportunistic communications,” in Intelligent Networking and Collaborative Systems (INCoS), 2012 4th International Conference on, Sept 2012, pp. 457–462.
  5. [5]  R. N. Lass, J. B. Kopena, E. A. Sultanik, D. N. Nguyen, C. P. Dugan, P. J. Modi, and W. C. Regli, “Coordination of first responders under communication and resource constraints,” in Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems-Volume 3, International Foundation for Autonomous Agents and Multiagent Systems, pp. 1409–1412, 2008.
  6. [6]  D. T. Nguyen, W. Yeoh, and H. C. Lau, “Stochastic dominance in stochastic dcops for risk-sensitive applications,” in Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, International Foundation for Autonomous Agents and Multiagent Systems, pp. 257–264, 2012.
  7. [7]  H. W. Hamacher and S. A. Tjandra, “Mathematical modelling of evacuation problems–a state of the art,” Pedestrian and evacuation dynamics, Vol.2002, No.227-266, pp. 1–2, 2002.
  8. [8]  Q. Lu, B. George, and S. Shekhar, “Capacity constrained routing algorithms for evacuation planning: A summary of results,” in Advances in spatial and temporal databases, Springer, pp. 291–307, 2005.
  9. [9]  T. Hadzic, K. N. Brown, and C. J. Sreenan, “Real-time pedestrian evacuation planning during emergency,” in Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on, IEEE, pp. 597–604, 2011.
  10. [10]  W. Zhang, G. Wang, Z. Xing, and L. Wittenburg, “Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks,” Artif. Intell., No.161, pp. 55–87, 2005.
  11. [11]  S. Fitzpatrick and L. Meertens, “An experimental assessment of a stochastic, anytime, decentralized, soft colourer for sparse graphs,” in 1st Symposium on Stochastic Algorithms: Foundations and Applications, pp. 49–64, 2001.
  12. [12]  P. J. Modi, W.-M. Shen, M. Tambe, and M. Yokoo, “Adopt: asynchronous distributed constraint optimization with quality guarantees,” Artif. Intell., No.161, pp. 149–180, 2005.
  13. [13]  A. Petcu and B. Faltings, “A scalable method for multiagent constraint optimization,” in IJCAI, Vol.5, pp. 266–271, 2005.
  14. [14]  C. M. Macal and M. J. North, “Tutorial on agent-based modeling and simulation,” in Proceedings of the 37th Conference on Winter Simulation, ser. WSC ’05, Winter Simulation Conference, pp. 2–15, 2005.
  15. [15]  C. Burstedde, K. Klauck, A. Schadschneider, and J. Zittartz, “Simulation of pedestrian dynamics using a two-dimensional cellular automaton,” Physica A: Statistical Mechanics and its Applications, Vol.295, No.3, pp. 507–525, 2001.
  16. [16]  D. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic,” Nature, Vol.407, No.6803, pp. 487–490, 2000.
  17. [17]  J. Shi, A. Ren, and C. Chen, “Agent-based evacuation model of large public buildings under fire conditions,” Automation in Construction, Vol.18, No.3, pp. 338–347, 2009.
  18. [18]  Y. Yoshida, T. Kimura, Y. Minegishi, and T. Sano, “Tsunami safe town planning with evacuation simulation,” Journal of Disaster Research, Vol.9, No.7, pp. 719–729, 2014.
  19. [19]  T. Kaneda and D. Okayama, “A pedestrian agent model using relative coordinate systems,” in Agent-Based Approaches in Economic and Social Complex Systems IV, Springer, pp. 63–70, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Jul. 23, 2019