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


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

July 31, 2015
October 21, 2015
December 1, 2015
disaster evacuation, distributed autonomous system, DCOP, multi-agent simulation

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.
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Last updated on Jan. 19, 2019