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Crowd Simulations and Determining the Critical Density Point of Emergency Situations

Published online by Cambridge University Press:  30 May 2017

Gholamreza Khademipour ,
Nouzar Nakhaee ,
Seyed Mohammad Saberi Anari ,
Maryam Sadeghi ,
Hojjat Ebrahimnejad  and
Hojjat Sheikhbardsiri
Gholamreza Khademipour
Affiliation:
Department of Emergency Management Center, Kerman University of Medical Sciences, Kerman, Iran
Nouzar Nakhaee
Affiliation:
Neuroscience Research Center, Kerman University of Medical Sciences, Kerman, Iran
Seyed Mohammad Saberi Anari
Affiliation:
Department of Emergency Management Center, Kerman University of Medical Sciences, Kerman, Iran
Maryam Sadeghi
Affiliation:
Department of Environmental Health, Kerman University of Medical Sciences, Kerman, Iran
Hojjat Ebrahimnejad
Affiliation:
Department of Development and Resource Management, Kerman University of Medical Sciences, Kerman, Iran
Hojjat Sheikhbardsiri*
Affiliation:
Health Management and Economic Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
*
Correspondence and reprint requests to Hojat Sheikhbardsiri, Department of Health Services Administration, Health Management and Economic Research Center, Isfahan University of Medical Sciences, Isfahan, Iran (e-mail: hojat.sheikhbardsiri@gmail.com).
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Abstract

Objective

In modern societies, crowds and mass gatherings are recurrent. A combination of inadequate facilities and inefficient population management can lead to injury and death. Simulating people’s behavior in crowds and mass gatherings can assist in the planning and management of gatherings, especially in emergency situations.

Methods

We aimed to determine the crowd pattern and the critical density point in the grand bazaar of Kerman in Iran. We collected data by use of a census method with a questionnaire. To determine the critical density point, height and weight data were placed in the equation $\,s\,{\equals}\,\sqrt {{{L{\vskip -1.5pt \,\,\asterisk\,\,}M} \over {3600}}} $ and the outer body surface of all the individuals in the bazaar was calculated. The crowd was simulated by use of flow-based modeling. Flow rate was determined by using the equation (flow rate=density * speed). By use of SketchUp Pro software (version 8; Trimble, Sunnyvale, CA), the movement of each person and the general flow rate were simulated in the three-dimensional environment of Kerman bazaar.

Results

Our findings showed that the population critical density point in Kerman bazaar would be 6112 people. In an accident, the critical density point in Kerman bazaar would be created in about 1 minute 10 seconds after the event.

Conclusion

It seems necessary to identify and provide solutions for reducing the risk of disasters caused by overcrowding in Kerman bazaar. It is suggested that researchers conduct studies to design safe and secure emergency evacuation of Kerman bazaar as well as proper planning for better and faster access of aid squads to this location. (Disaster Med Public Health Preparedness. 2017;11:674–680)

Keywords

simulationcritical density pointemergency
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 11 , Issue 6 , December 2017 , pp. 674 - 680
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2017 

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