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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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References

1. Fruin, JJ. The causes and prevention of crowd disasters. Presented at: First International Conference on Engineering for Crowd Safety; 2002; London.Google Scholar
2. Friberg, M, Hjelm, M. Mass evacuation—human behavior and crowd dynamics—What do we know? Lund University Student Papers. Lund University Library. https://lup.lub.lu.se/student-papers/search/publication/7766859. Published 2015. Accessed March 31, 2017.Google Scholar
3. Abubakar, I, Gautret, P, Brunette, GW, et al. Global perspectives for prevention of infectious diseases associated with mass gatherings. Lancet Infect Dis. 2012;12(1):66-74.Google Scholar
4. De Lorenzo, RA. Mass gathering medicine: a review. Prehosp Disaster Med. 1997;12(1):68-72. https://doi.org/10.1017/S1049023X00037250.Google Scholar
5. Soomaroo, L, Murray, V. Disasters at mass gatherings: lessons from history [published online March 12, 2012]. PLoS Curr. doi: 10.1371/currents.RRN1301.CrossRefGoogle Scholar
6. WHO. Public Health for Mass Gatherings: Key Considerations. Geneva: WHO Publications; 2015:12–22.Google Scholar
7. Klupfel, H. The simulation of crowd dynamics at very large events–calibration, empirical data, and validation. In: Waldau N, Gattermann P, Knoflacher H, et al, eds. Pedestrian and Evacuation Dynamics 2005. Berlin: Springer; 2007:285-296.Google Scholar
8. Reicher, S. The psychology of crowd dynamics. In: Hogg MA, Tindale RS, eds. Blackwell Handbook of Social Psychology: Group Processes. Blackwell Publishers; 2008:182-208.Google Scholar
9. Oberhagemann, BD. Static and dynamic crowd densities at major public events. Technical Report vfdb TB 13-01, German Fire Protection Association, 2012.Google Scholar
10. Re, Ö, Rouz, K. From bazaars to shopping centers. Mediterr J Soc Sci. 2014;5(23):1875-1881.Google Scholar
11. Foudil Cherif, ND. A framework to simulate the evacuation of a crowd in emergency situations. Georg Electron Sci J. 2006;1(1):2006.Google Scholar
12. Kuligowski, ED. The process of human behavior in fires. NIST Technical Note (NIST TN) 1632. https://www.nist.gov/publications/process-human-behavior-fires-0. Published May 15, 2009. Accessed March 31, 2017.Google Scholar
13. Hassan, S, Elyasi, L, Rashidzade, A. Determination of clavicle bone length to height ratio in 20-30 year-old men and women in Kerman Background and Aim: in order to make a dimensional proportion between human and equipment or environment, anthropometric data bank is essential. Anthropomet. 2014;3(1):8-14.Google Scholar
14. Almeida, JE, Rosseti, R, Coelho, AL. Crowd simulation modeling applied to emergency and evacuation simulations using multi-agent systems. arXiv:1303.4692. https://arxiv.org/abs/1303.4692. Submitted March 15, 2013. Accessed April 3, 2017.Google Scholar
15. Still, GK. Crowd Dynamics [PhD thesis]. Coventry, United Kingdom: University of Warwick; 2000.Google Scholar
16. Jordao, K, Charalambous, P, Christie, M, Pettr, J, Jordao, K, Charalambous, P, et al. Crowd Art: Density and Flow Based Crowd Motion Design. Presented at: 8th ACM SIGGRAPH Conference on Motion in Games, MIG ’15; November 2015; Paris, France. doi: 10.1145/2822013.2822023.Google Scholar
17. Shendarkar, A, Vasudevan, K, Lee, S, Son, YJ. Crowd simulation for emergency response using BDI agent based on virtual reality. In: Proceedings of the 38th Winter Simulation Conference. 2006:545-553. https://doi.org/10.1109/WSC.2006.323128.Google Scholar
18. Pellegrini, S, Gall, J, Sigal, L, Gool, L Van. Destination flow for crowd simulation. In: Fusiello A, Murino V, Cucchiara R, eds. Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science . Vol 7585. Berlin: Springer; 2012. https://doi.org/10.1007/978-3-642-33885-4_17.Google Scholar
19. Helbing, D. Simulation of pedestrian crowds in normal and evacuation situations. In: Schreckenberg M, Sharma SD, eds. Pedestrian and Evacuation Dynamics. Berlin: Springer; 2002:21-58.Google Scholar
20. Yu, W, Johansson, A. Modeling crowd turbulence by many-particle simulations. Phys Rev E. 2007;76(4):046105. https://doi.org/10.1103/PhysRevE.76.046105.Google Scholar
21. Jablonski, K, Argyriou, V. Crowd simulation for dynamic environments based on information spreading and agents’ personal interests. Transp Res Procedia. 2014;2:412-417. https://doi.org/10.1016/j.trpro.2014.09.046.Google Scholar