CROWD DYNAMICS ANALISYS: SIMULATING HETEREOGENEOUS CROWDS WITH PANIC EFFECT STOCHASTICS BEHAVIOUR
DOI:
https://doi.org/10.4314/jfas.v11i2.19Keywords:
crowd dynamics, multi-agent simulation, swarm, emergent behaviorAbstract
In a crowd, where the density might reach one person per square meter and above, the mass of individuals moves in a way that may potentially induce panic amongst individuals, or hazards of personal injuries, from slight to fatal. A computer simulation is implemented and conducted in order to study and analyze the dynamics and behavior of crowds, both at micro- and macro-levels. The simulation is comprised of multiple arenas with different layouts, as well as different compositions of heterogeneous agent behavior. The simulations are observed to conform to established results on a localized scale, and the statistical data shows no significant increase in total evacuation time with increasing composition of non-interactive, path-finding agents amongst flocking agents.
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References
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[2] S. D. Reicher, “The Psychology of Crowd Dynamics”, in Blackwell Handbook of Social Psychology: Group Processes, Mass., Blackwell, ch. 8, pp. 182, 2001.
[3] A. Sud, R. Gayle, S. Guy, E. Andersen, M. Lin and D. Manocha, “Real-time Simulation of Heterogeneous Crowds”, Tech. rep., University of north california, 2007.
[4] F. van Wageningen-Kessels, L. Leclercq, W. Daamen, S. P. Hoogendoorn, “The lagrangian coordinate system and what it means for two-dimensional crowd flow models”, Physica A: Statistical Mechanics and its Applications, 443, pp. 272-285, 2016. https://doi.org/10.1016/j.physa.2015.09.048
[5] H. Jiang, W. Xu, T. Mao, C. Li, S. Xia, Z. Wang, “Continuum crowd simulation in complex environments”, Computers & Graphics, 34, (5), pp. 537-544, 2010. https://doi.org/10.1016/j.cag.2010.05.013
[6] A. Treuille, S. Cooper, Z. Popović, “Continuum crowds”, ACM Transactions on Graphics (TOG), 25, (3), pp. 1160-1168, 2006. https://doi.org/10.1145/1141911.1142008
[7] K. Chavan, A. Dhanawade, S. Sonavane, “Case Simulation of Pedestrian Movements using Cellular Automata Model”, International Journal of Computer Science and Mobile Computing, 4, (5), pp. 107-112, 2015.
[8] V. S. Kalogeiton, D. P. Papadopoulos, I. P. Georgilas, G. C. Sirakoulis, A. I. Adamatzky, “Biomimicry of Crowd Evacuation with a Slime Mould Cellular Automaton Model”, Computational Intelligence, Medicine and Biology, Springer, Cham, pp. 123-151, 2015. https://doi.org/10.1007/978-3-319-16844-9_7
[9] X. Bai, Y. Qian, “Security Model of Stadium Evacuation Combined with Multi-agent and Cellular Automata”, International Journal of Security and Its Applications, 9, (2), pp. 135-148, 2015. https://doi.org/10.14257/ijsia.2015.9.2.13
[10] C. Y. Ozcan, M. Haciomeroglu, “A path-based multi-agent navigation model”, The Visual Computer, 31, (6-8), pp. 863-872, 2015. https://doi.org/10.1007/s00371-015-1110-2
[11] J. E. Godoy, I. Karamouzas, S. J. Guy, M. Gini, “Adaptive Learning for Multi-Agent Navigation”, in Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, pp. 1577-1585, 2015.
[12] X. Pan, C. S. Han, K. Dauber, K. H. Law, “A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations”, AI & Society, 22, (2), pp. 113-132, 2007. https://doi.org/10.1007/s00146-007-0126-1
[13] D. Helbing, I. Farkas, T. Vicsek, “Simulating dynamical features of escape panic”, Nature, 407, (6803), pp. 487-490, 2000. https://doi.org/10.1038/35035023
[14] S. J. Abu-Bakar, W. A. F. W. Othman, S. S. N. Alhady, “An Analysis of Heterogeneous Swarm Evacuation Model”, In 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, Springer, Singapore, pp. 141-148, 2017. https://doi.org/10.1007/978-981-10-1721-6_16
[15] S. Tisue, U. Wilensky, “Netlogo: A simple environment for modeling complexity”, in International Conference on Complex Systems, (21), pp. 16-21, 2004.
[16] S. Tisue, U. Wilensky, “NetLogo: Design and implementation of a multi-agent modeling environment”, in Proceedings of Agent, pp. 1-10, 2004.
[17] J. Zhou, X. Wua, W. Yu, M. Small, J. Lua, “Flocking of multi-agent dynamical systems based on pseudo-leader mechanism”, Systems & Control Letters, 61, (1), pp. 195-202, 2012. https://doi.org/10.1016/j.sysconle.2011.10.006
[18] R. Olfati-Saber, “Flocking for multi-agent dynamic systems: Algorithms and theory”, IEEE Transactions on Automatic Control, 51, (3), pp. 401-420, 2006. https://doi.org/10.1109/TAC.2005.864190
[19] C. W. Reynolds, “Flocks, herds and schools: A distributed behavioral model”, in Proceedings of 14th Annual Conference on Computer Graphics and Interactive Techniques, New York, NY, pp. 25-34. 1987. https://doi.org/10.1145/37402.37406
[20] J. Toner, Y. Tu, “Flocks, herds, and schools: A quantitative theory of flocking”, Physical Review E, 58, (4), p. 4828, 1998. https://doi.org/10.1103/PhysRevE.58.4828
[21] T. Vicsek, “Universal patterns of collective motion from minimal models of flocking”, in Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pp. 3-11, 2008. https://doi.org/10.1109/SASO.2008.23
[22] P. Basu, J. Redi, V. Shurbanovet, “Coordinated flocking of UAVs for improved connectivity of mobile ground nodes”, in Military Communications Conference, 3, pp. 1628-1634, 2004. https://doi.org/10.1109/MILCOM.2004.1495182
[23] S. Momen, B. P. Amavasai, N. H. Siddique, “Mixed species flocking for heterogeneous robotic swarms”, in EUROCON 2007, The International Conference on Computer as a Tool, pp. 2329-2336, 2007. https://doi.org/10.1109/EURCON.2007.4400455
[24] W. A. F. W. Othman, B. P. Amavasai, S. P. McKibbin, F. Caparrelli, “An Analysis of Collective Movement Models for Robotic Swarms”, in EUROCON 2007, The International Conference on Computer as a Tool, pp. 2373-2380, 2007. https://doi.org/10.1109/EURCON.2007.4400556
[25] J. M. Thiollay, “Frequency of mixed species flocking in tropical forest birds and correlates of predation risk: an intertropical comparison”, Journal of Avian Biology, 30, (3), 282-294, 1999. https://doi.org/10.2307/3677354
[26] A. Johansson, M. Batty, K. Hayashi, O. Al-Bar, D. Marcozzi, Z. A. Memish, “Crowd and environmental management during mass gatherings”, The Lancet Infectious Diseases, 12, (2), pp. 150-156, 2012. https://doi.org/10.1016/S1473-3099(11)70287-0
[27] B. Zhou, X. Tang, X. Wang, “Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents”, International Journal of Computer Vision, 111, (1), pp. 50-68, 2015. https://doi.org/10.1007/s11263-014-0735-3
[28] T. Terzimehic, S. Silajdzic, V. Vajnberger, J. Velagic, N. Osmic, “Path finding simulator for mobile robot navigation”, in XXIII International Symposium on Information, Communication and Automation Technologies (ICAT), IEEE, pp. 1-6, 2011. https://doi.org/10.1109/ICAT.2011.6102086
[29] D. Silver, “Cooperative Pathfinding”, in Proceeding AIIDE'05 Proceedings of the First AAAI Conference on Artificial Intelligence and Interactive Digital Entertainmen, pp. 117-122, 2005.
[30] X. Cui, H. Shi, “A*-based Pathfinding in Modern Computer Games”, International Journal of Computer Science and Network Security, 11, (1), pp. 125-130, 2011.
[31] H. H. Kelley, J. C. Condry Jr, A. E. Dahlke, A. H. Hill, “Collective behavior in a simulated panic situation”, Journal of Experimental Social Psychology, 1, (1), pp. 20-54, 1965. https://doi.org/10.1016/0022-1031(65)90035-1
[32] D. Helbing, I. J. Farkas, P. Molnár, T. Vicsek, “Simulation of pedestrian crowds in normal and evacuation situations”, Pedestrian and Evacuation Dynamics, 21, (2), pp. 21-58, 2002.
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Published
2019-04-20
How to Cite
TAN, Z.; OTHMAN, W. A. F. W.; WAHAB, A. A. A.; ALHADY, S. S. N. CROWD DYNAMICS ANALISYS: SIMULATING HETEREOGENEOUS CROWDS WITH PANIC EFFECT STOCHASTICS BEHAVIOUR. Journal of Fundamental and Applied Sciences, [S. l.], v. 11, n. 2, p. 838–856, 2019. DOI: 10.4314/jfas.v11i2.19. Disponível em: https://jfas.info/index.php/JFAS/article/view/337. Acesso em: 30 jan. 2025.
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