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Conceptualizing intragroup and intergroup dynamics within a controlled crowd evacuation

Terra Elzie, ME, Erika Frydenlund, MS, Andrew J. Collins, PhD, R. Michael Robinson, PhD

Abstract


Social dynamics play a critical role in successful pedestrian evacuations. Crowd modeling research has made progress in capturing the way individual and group dynamics affect evacuations; however, few studies have simultaneously examined how individuals and groups interact with one another during egress. To address this gap, the researchers present a conceptual agent-based model (ABM) designed to study the ways in which autonomous, heterogeneous, decision-making individuals negotiate intragroup and intergroup behavior while exiting a large venue. A key feature of this proposed model is the examination of the dynamics among and between various groupings, where heterogeneity at the individual level dynamically affects group behavior and subsequently group/group interactions. ABM provides a means of representing the important social factors that affect decision making among diverse social groups. Expanding on the 2013 work of Vizzari et al., the researchers focus specifically on social factors and decision making at the individual group and group/group levels to more realistically portray dynamic crowd systems during a pedestrian evacuation. By developing a model with individual, intragroup, and intergroup interactions, the ABM provides a more representative approximation of real-world crowd egress. The simulation will enable more informed planning by disaster managers, emergency planners, and other decision makers. This pedestrian behavioral concept is one piece of a larger simulation model. Future research will build toward an integrated model capturing decision-making interactions between pedestrians and vehicles that affect evacuation outcomes.


Keywords


agent-based model, decision-making, evacuation, pedestrian, simulation

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References


Liao M, Sun J, Li K, et al.: Framework of agent-based pedestrian traffic simulation system. Logistics. 2009: 1763-1769.

Vizzari G, Manenti L, Ohtsuka K, et al.: An agent-based approach to pedestrian and group dynamics: Experimental and real world scenarios. Paper presented at 7th International Workshop on Agents in Traffic and Transportation, Ontario, Canada, 2012.

Vizzari G, Manenti L, Crociani L: Adaptive pedestrian behavior for the preservation of group cohesion. Complex Adapt Syst Model. 2013; 1(1): 1-29.

Gilbert GN: Agent-Based Models. Los Angeles: Sage Publications, 2008.

Epstein JM: Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton: Princeton University Press, 2006.

TraffGo HT: Pedgo. 2013. Available at http://www.traffgo-ht.com/en/pedestrians/products/pedgo. Accessed December 2013.

Grandison A, Muthu Y, Lawrence P, et al.: Simulating the evacuation of very large populations in large domains using a parallel implementation of the building EXODUS evacuation model. Paper presented at Proceedings of the 11th International Fire Science & Engineering Conference, Interflam, 2007.

Park J, Gwynne S, Galea E, et al.: Validating the building EXODUS evacuation model using data from an unannounced trial evacuation. Paper presented at Proceedings of the 2nd International Conference on Pedestrian and Evacuation Dynamics, Greenwich, UK, 2003.

Metavr: Virtual Reality Scene Generator. 2013. Available at www.metavr.com. Accessed December 2013.

Still K: Myriad II. 2008. Available at www.crowddynamics.com/technical. Accessed December 2013.

Bauer D, Seer S, Brändle N: Macroscopic pedestrian flow simulation for designing crowd control measures in public transport after special events. Paper presented at SCSC '07 Proceedings of the 2007 Summer Computer Simulation Conference, San Diego, CA, 2007.

Bhat S, Maciejewski AA: An agent-based simulation of the LA 1992 riots. Paper presented at Conference on Artificial Intelligence (ICAI'06), Boston, MA, 2006.

Sud A, Andersen E, Curtis S, et al.: Real-time path planning for virtual agents in dynamic environments. Paper presented at ACM SIGGRAPH 2008 Classes, Los Angeles, CA, 2008.

Henderson L: On the fluid mechanics of human crowd motion. Transp Res. 1974; 8(6): 509-515.

Helbing D: A fluid dynamic model for the movement of pedestrians. Complex Syst. 1992; 6: 391-415.

Treuille A, Cooper S, Popovic´ Z: Continuum crowds. ACM Trans. Graph. 2006; 25: 1160-1168.

Hanisch A, Tolujew J, Richter K, et al.: Online simulation of pedestrian flow in public buildings. Paper presented at Proceedings of the 2003 Winter Simulation Conference, New Orleans, LA, 2003.

Løvås GG: Modeling and simulation of pedestrian traffic flow. Transp Res Part B: Methodol. 1994; 28(6): 429-443.

Teknomo K, Takeyama Y, Inamura H: Review on microscopic pedestrian simulation model. Paper presented at Proceedings Japan Society of Civil Engineering Conference, Morioka, Japan, March 2000.

Helbing D, Molnár P: Social force model for pedestrian dynamics. Phys Rev E. 1995; 51(5): 4282-4286.

Helbing D, Farkas I, Vicsek T: Simulating dynamical features of escape panic. Nature. 2000; 407(6803): 487-490.

Helbing D, Farkas IJ, Molnár P, et al.: Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian Evacuation Dyn. 2002; 21: 21-58.

Gipps PG, Marksjö B: A micro-simulation model for pedestrian flows. Math Comput Simul. 1985; 27(2): 95-105.

Klüpfel H, Meyer-König T, Wahle J, et al.: Microscopic simulation of evacuation processes on passenger ships. Theory Pract Issues Cell Automata. 2001: 63-71.

Szilagyi MN, Szilagyi ZC: A tool for simulated social experiments. Simulation. 2000; 74(1): 4-10.

Okazaki S: A study of pedestrian movement in architectural space, Part 1: Pedestrian movement by the application on of magnetic models. Trans AIJ. 1979; 283(3): 111-119.

Reynolds CW: Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Comput Graph. 1987; 21(4): 25-34.

Reynolds CW: Steering behaviors for autonomous characters. Paper presented at Game Developers Conference, San Jose, CA, 1999.

Powers WT: Behavior: The Control of Perception. Chicago: Aldine Pub. Co., 1973.

McPhail C, Powers WT, Tucker CW: Simulating individual and collective action in temporary gatherings. Soc Sci Comput Rev. 1992; 10(1): 1-28.

Lewin K, Cartwright D: Field Theory in Social Science: Selected Theoretical Papers. London: Tavistock, 1952.

Mirabet V, Auger P, Lett C: Spatial structures in simulations of animal grouping. Ecol Modell. 2007; 201(3-4): 468-476.

Okubo A: Dynamical aspects of animal grouping: Swarms, schools, flocks, and herds. Adv Biophys. 1986; 22(0): 1-94.

Pelechano N, Stocker C, Allbeck J, et al.: Being a part of the crowd: Towards validating VR crowds using presence. Paper presented at AAMAS '08 Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, Vol 1, Richland, SC, 2008.

Lakoba TI, Kaup DJ, Finkelstein NM: Modifications of the Helbing-Molnar-Farkas-Vicsek social force model for pedestrian evolution. Simulation. 2005; 81(5): 339-352.

Miller JH, Page SE: Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Illustrated edition ed. Princeton: Princeton University Press, 2007.

Epstein JM, Axtell R: Growing Artificial Societies: Social Science from the Bottom Up. Washington, DC: Brookings Institution Press, 1996.

Goodwin P, Wright G: Decision Analysis for Management Judgment. 3 ed. Chichester, West Sussex: Wiley, 1991.

Eatwell J, Milgate M, Newman P: The New Palgrave: Game Theory. London: Macmillan Press, 1987.

Helbing D, Johansson A, Al-Abideen HZ: Dynamics of crowd disasters: An empirical study. Phys Rev E. 2007; 75(4): 046109.

Army U: Pedestrian studies. In FM 19-25 Military Police Traffic Operations. Washington, DC: Department of the Army, 1977.

Chu J-x, Li J-j, Xu M, et al.: Simulating Escape Panic Based on the Mechanism of Asymmetric Information Distribution. Santa Fe, NM: Santa Fe Institute, 2005.

Shiwakoti N, Sarvi M, Rose G, et al.: Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions. Transp Res Part B: Methodol. 2011; 45(9): 1433-1449.

Yu W, Johansson A: Modeling crowd turbulence by many-particle simulations. Phys Rev E. 2007; 76(4): 046105.

Kirkland JA, Maciejewski AA: A simulation of attempts to influence crowd dynamics. Paper presented at IEEE International Conference on Systems, Man and Cybernetics, Washington, DC, 2003.

Frydenlund E, Elzie T, Collins A, et al.: A hybridized approach to validation: The role of sociological research methods in pedestrian modeling. Paper presented at PED2014: 7th International Conference on Pedestrian and Evacuation Dynamics, Delft, the Netherlands, October 22-24, 2014.

Thalmann DMSR: Crowd simulation [eBook]. 2007; doi:10.1007/978-1-84628-825-8.

Curtis S, Guy SJ, Zafar B, et al.: A case study in simulating the behavior of dense, heterogeneous crowds. Paper presented at 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, November 6-13, 2011.

Wijermans N, Jorna R, Jager W, et al.: CROSS: Modelling crowd behaviour with social-cognitive agents. J Artif Soc Soc Simul. 2013; 16(4): 1.

Hall ET: The Silent Language. Garden City, NY: Doubleday, 1959.

Braun A, Musse SR, de Oliveira LPL, et al.: Modeling individual behaviors in crowd simulation. Paper presented at 16th International Conference on Computer Animation and Social Agents, New Brunswick, NJ, 2003.

Braun A, Bodmann BE, Musse SR: Simulating virtual crowds in emergency situations. Paper presented at VRST '05 ACM symposium on Virtual Reality Software and Technology, Monterey, CA, 2005.

Brogan D, Hodgins J: Group behaviors for systems with significant dynamics. Auton Robots. 1997; 4(1): 137-153.

Villamil MB, Musse SR, Luna de Oliveira L: A model for generating and animating groups of virtual agents. In Rist T, Aylett R, Ballin D, Rickel J (eds.): Intelligent Virtual Agents. Vol 2792. Berlin/Heidelberg: Springer, 2003: 164-169.

Mataric´ MJ: Designing and understanding adaptive group behavior. Adapt Behav. 1995; 4(1): 51-80.

Miles R, Hamilton K: Learning UML 2.0 - A Pragmatic Introduction to UML. 1st ed. Sebastopol, CA: O'Reilly Media, Inc., 2006.

Granovetter M: Threshold models of collective behavior. Am J Sociol. 1978; 83(6): 1420-1443.

Still GK: Crowd Dynamics [dissertation]. Coventry, UK: Mathematics Institute, University of Warwick, 2000.

Wilensky U: NetLogo [computer program]. Evanston, IL: Northwestern University, 1999.

North MJ, Collier NT, Ozik J, et al.: Complex adaptive systems modeling with Repast Simphony. Complex Adapt Syst Model. 2013; 1: 3.

Railsback SF, Lytinen SL, Jackson SK: Agent-based simulation platforms: Review and development recommendations. Simulation. 2006; 82(9): 609-623.




DOI: https://doi.org/10.5055/jem.2015.0224

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