This paper focuses on designing a tool for guiding a group of people out of a public building when they are faced with dangerous situations that require immediate evacuation. Despite architectural attempts to produce safe floor plans and exit door placements, people will still commit to fatal route decisions. Since they have access to global views, we believe supervisory people in the control room can use our simulation tools to determine the best courses of action for people. Accordingly, supervisors can guide people to safety. In this paper, we combine Coulomb’s electrical law, graph theory, and convex and centroid concepts to demonstrate a computer-generated evacuation scenario that divides the environment into different safe boundaries around the locations of each exit door in order to guide people through exit doors safely and in the most expedient time frame. Our mechanism continually updates the safe boundaries at each moment based on the latest location of individuals who are present inside the environment. Guiding people toward exit doors depends on the momentary situations in the environment, which in turn rely on the specifications of each exit door. Our mechanism rapidly adapts to changes in the environment in terms of moving agents and changes in the environmental layout that might be caused by explosions or falling walls. 1. Introduction The gathering of a group of people at the same location and time is called a crowd. People who form the crowd often share a common activity. In order to study crowd evacuation, we need to have clear understanding of all their relevant attributes. Determining crowd dynamics based on their psychological identifications is one of the many ways that is broadly explored by previous researches. Crowd dispersion is unpredictable. There are many studies conducted that explore crowd behavior from different perspectives. One of the major crowd behaviors with psychological underpinnings is identified as deindividuation, which is the situation where antinormative individual behavior is exhibited in groups in which individuals are not seen as separate individuals. Simply put, deindividuation is blending in a group such that the individual decision making ceases to be observed separately. Submergence is largely acknowledged as the root of contemporary theories of deindividuation, [1]. Numerous studies have explored relationships between deindividuation and behavioral changes [1–6]. It is important to understand how individuals conceive themselves in the crowd since once being a member of a crowd they no longer act
References
[1]
F. J. Cannavale, H. A. Scarr, and A. Pepitone, “Deindividuation in the small group: further evidence,” Journal of Personality and Social Psychology, vol. 16, no. 1, pp. 141–147, 1970.
[2]
J. E. Singer, C. A. Brush, and S. C. Lublin, “Some aspects of deindividuation: identification and conformity,” Journal of Experimental Social Psychology, vol. 1, no. 4, pp. 356–378, 1965.
[3]
P. G. Zimbardo, “The human choice: individuation, reason, and order versus deindividuation, impulse and chaos,” in Proceedings of the Nebraska Symposium on Motivation, University of Nebraska Press, Lincoln, Neb, USA, 1969.
[4]
E. Diener, “Deindividuation, self-awareness, and disinhibition,” Journal of Personality and Social Psychology, vol. 37, no. 7, pp. 1160–1171, 1979.
[5]
K. Gergen, M. Gergen, and W. Barton, “Deviance in the dark,” Psychology Today, vol. 7, pp. 129–130, 1973.
[6]
R. D. Johnson and L. L. Downing, “Deindividuation and valence of cues: effects on prosocial and antisocial behavior,” Journal of Personality and Social Psychology, vol. 37, no. 9, pp. 1532–1538, 1979.
[7]
C. S. Carver and M. F. Scheier, Attention and Self-Regulation: A Control Theory Approach to Human Behavior, Springer, New York, NY, USA, 1981.
[8]
A. Fenigstein, M. F. Scheier, and A. H. Buss, “Public and private self consciousness: assessment and theory,” Journal of Consulting and Clinical Psychology, vol. 43, no. 4, pp. 522–527, 1975.
[9]
S. Prentice-Dunn and R. W. Rogers, “Deindividuation and the self-regulation of behavior,” in The Psychology of Group Influence, P. Paulus, Ed., Erlbaum, Hillsdale, NJ, USA, 1989.
[10]
G. Proulx, “Building Egress Using Photoluminescent Markings,” 2011, Construction Technology Update No. 78, National Research Council of Canada, 2011.
[11]
T. Korhonen and S. Hostika, “Modeling social interactions in fire evacuation,” in Proceedings 7th International Conference on Performance-Based Codes and Fire Safety Design Methods, 2008.
[12]
H. B. Sharbini and A. Bade, “Analysis of crowd behaviour theories in panic situation,” in Proceedings of the International Conference on Information and Multimedia Technology (ICIMT '09), pp. 371–375, December 2009.
[13]
V. K. Singh and A. K. Gupta, “Agent based models of social systems and collective intelligence,” in Proceedings of the International Conference on Intelligent Agent and Multi-Agent Systems (IAMA '09), July 2009.
[14]
L. E. Aik, “Exit-selection behaviors during a classroom evacuation,” International Journal of Physical Sciences, vol. 6, no. 13, pp. 3218–3231, 2011.
[15]
R. D. Peacock, J. D. Averill, and E. D. Kuligowski, “Stairwell evacuation from buildings: what we know we don't know,” NIST Technical Note NIST TN 1624, 2009.
[16]
Y. Zhao and S. A. Billings, “Neighborhood detection using mutual information for the identification of cellular automata,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 36, no. 2, pp. 473–479, 2006.
[17]
C. A. Perez-Delgado and D. Cheung, Models of Quantum Cellular Automata, Quantum Physics, 2009.
[18]
A. Braun, S. Musse, Oliveira, and E. Bardo, “Modeling Individual behaviors in Crowd simulation,” in Proceedings of the 16th International Conference on Computer Animation and Social Agents, IEE Computer Society, 2003.
[19]
H. Yeh, S. Curtis, S. Patil, J. P. Van den Berg, D. Manocha, and M. C. Lin, “Composite agents,” in Proceedings of the Symposium on Computer Animation, Eurographics Association, 2008.
[20]
S. J. Guy, S. Kim, M. C. Lin, and D. Manocha, “Simulating heterogeneous crowd behaviors using personality trait theory,” in Proceedings of the International Symposium on Computer Animation, Eurographics Association, 2011.
[21]
C. Dogbe, “On the modeling of crowd dynamics by generalized kinetic models,” Journal of Mathematical Analysis and Applications, vol. 18, supplement 1, pp. 1317–1345, 2008.
[22]
X. Zheng, T. Zhong, and M. Liu, “Modeling crowd evacuation of a building based on seven methodological approaches,” Building and Environment, vol. 44, no. 3, pp. 437–445, 2009.
[23]
A. Johansson, D. Helbing, H. Z. Al-Abideen, and S. Al-Bosta, “From crowd dynamics to crowd safety: a video-based analysis,” Advances in Complex Systems, vol. 11, no. 4, pp. 497–527, 2008.
[24]
J. Cole and J. Zhuang, Decisions in Disaster Recovery Operations: A Game Theoretic Perspective on Organization Perspective, Berkeley Electronic Press, 2011.
[25]
R. D. Peacock, J. D. Averill, and E. D. Kuligowski, “Egress from the World Trade Center Towers on September 11, 2001,” in Fire Technology, Springer, 2011.
[26]
H. C. Huang, S. M. Lo, C. M. Zhao, and P. Wang, “Simulation of occupant exit selection behavior during emergency evacuation using a game theory model,” in Proceedings of the 8th WSEAS International Conference on Automatic Control, Modeling and Simulation, pp. 207–212, Prague, Czech Republic, 2006.
[27]
N. Pelechano, J. M. Allbeck, and N. I. Badler, Virtual Crowds: Methods, Simulation and Control, Morgan and Claypool Publishers, 2008.
[28]
J. Pan, L. Zhang, M. C. Lin, and D. Manocha, “A hybrid approach for simulating human motion in constrained environments,” Computer Animation and Virtual Worlds, vol. 21, no. 3-4, pp. 137–149, 2010.
[29]
S. Patil, J. Van Den Berg, S. Curtis, M. C. Lin, and D. Manocha, “Directing crowd simulations using navigation fields,” IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 2, pp. 244–254, 2011.
[30]
D. C. Brogan and J. K. Hodgins, “Group behaviors for systems with significant dynamics,” Autonomous Robots, vol. 4, no. 1, pp. 137–153, 1997.
[31]
T. Sakuma, T. Mukai, and S. Kuriyama, “Psychological model for animating crowded pedestrians,” Computer Animation and Virtual Worlds, vol. 16, no. 3-4, pp. 343–351, 2005.
[32]
N. Pelechano, J. M. Allbeck, and N. I. Badler, “Controlling individual agents in high-density crowd simulation,” in Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 99–108, 2007.
[33]
S. R. Musse and D. Thalmann, “A model of human crowd behavior: group inter-relationship and collision detection analysis,” in Computer Animation and Simulation, pp. 39–51, 1997.
[34]
W. Shao and D. Terzopoulos, “Autonomous pedestrians,” in Proceedings of the ACM SIGGRAPH/5th Eurographics Symposium on Computer Animation, pp. 19–28, July 2005.
[35]
Q. Yu and D. Terzopoulos, “A decision network framework for the behavioral animation of virtual humans,” in Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 119–128, 2007.
[36]
S. Paris, J. Pettré, and S. Donikian, “Pedestrian reactive navigation for crowd simulation: a predictive approach,” Computer Graphics Forum, vol. 26, no. 3, pp. 665–674, 2007.
[37]
M. Sung, M. Gleicher, and S. Chenney, “Scalable behaviors for crowd simulation,” Computer Graphics Forum, vol. 23, no. 3, pp. 519–528, 2004.
[38]
E. Bouvier, E. Cohen, and L. Najman, “From crowd simulation to airbag deployment: particle systems, a new paradigm of simulation,” Journal of Electronic Imaging, vol. 6, no. 1, pp. 94–107, 1997.
[39]
G. K. Still, Crowd dynamics [Ph.D. thesis], Warwick University, 2000.
[40]
P. A. Thompson and E. W. Marchant, “A computer model for the evacuation of large building populations,” Fire Safety Journal, vol. 24, no. 2, pp. 131–148, 1995.
[41]
S. Bandini, M. L. Federici, and G. Vizzari, “Situated cellular agents approach to crowd modeling and simulation,” Cybernetics and Systems, vol. 38, no. 7, pp. 729–753, 2007.
[42]
D. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic,” Nature, vol. 407, no. 6803, pp. 487–490, 2000.
[43]
B. Adriana, S. R. Musse, P. L. Luiz, and E. J. De Oliveira, “Bardo: modeling individual behaviors in crowd simulation,” in Proceedings of Computer Animation and Social Agents, pp. 143–148, 2003.
[44]
F. I. Stahl, “BFIRES-II: a behavior based computer simulation of emergency egress during fires,” Fire Technology, vol. 18, no. 1, pp. 49–65, 1982.
[45]
L. Y. Cooper, “A mathematical model for estimating available safe egress time in fires,” Fire and Materials, vol. 6, no. 3-4, pp. 135–144, 1982.
[46]
T. M. Kisko and R. L. Francis, “EVACNET+: a computer program to determine optimal building evacuation plans,” Fire Safety Journal, vol. 9, no. 2, pp. 211–220, 1985.
[47]
S. M. Olenick and D. J. Carpenter, “An updated international survey of computer models for fire and smoke,” Journal of Fire Protection Engineering, vol. 13, no. 2, pp. 87–110, 2003.
[48]
R. Friedman, “An international survey of computer models for fire and smoke,” Journal of Fire Protection Engineering, vol. 4, no. 3, pp. 81–92, 1992.
[49]
J. M. Watts, “Computer models for evacuation analysis,” Fire Safety Journal, vol. 12, no. 3, pp. 237–245, 1987.
[50]
D. Helbing and P. Molnár, “Social force model for pedestrian dynamics,” Physical Review E, vol. 51, no. 5, pp. 4282–4286, 1995.
[51]
P. Gawroński and K. Ku？akowski, “Crowd dynamics—being stuck,” Computer Physics Communications, vol. 182, no. 9, pp. 1924–1927, 2011.
[52]
T. Korhonen, S. Heliovaara, S. Hostikkaa, and H. Ehtamo, “Counterflow model for agent-based simulation of crowd dynamics,” in Safety Science, 2010.
[53]
N. Shiwakoti, M. Sarvi, G. Rose, and M. Burd, “Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions,” Transpostation Research B, vol. 45, no. 9, p. 1433, 2011.
[54]
C. M. Henein and T. White, “Microscopic information processing and communication in crowd dynamics,” Physica A, vol. 389, no. 21, pp. 4636–4653, 2010.
[55]
R. D. Peacock, E. D. Kuligowski, and J. D. Averill, Pedestrian and Evacuation Dynamics, Springer, 2011.
[56]
B. Steffen and A. Seyfried, “Methods for measuring pedestrian density, flow, speed and direction with minimal scatter,” Physica A, vol. 389, no. 9, pp. 1902–1910, 2010.
[57]
N. Bellomo and C. Dogbe, “On the modeling of traffic and crowds: a survey of models, speculations, and perspectives,” SIAM Review, vol. 53, no. 3, pp. 409–463, 2011.
[58]
X. Zheng and Y. Cheng, “Modeling cooperative and competitive behaviors in emergency evacuation: a game-theoretical approach,” Computers & Mathematics with Applications, vol. 62, no. 12, pp. 4627–4634, 2011.
[59]
R. Colombo, P. Goatin, and M. Rosini, “A macroscopic model of pedestrian flows in panic situations,” in Current Advances in Nonlinear Analysis and Related Topics, Mathematical Sciences and Applications, pp. 43–60, Gakuto, 2010.
[60]
C. Dogbe, “On the Cauchy problem for macroscopic model of pedestrian flows,” Journal of Mathematical Analysis and Applications, vol. 372, no. 1, pp. 77–85, 2010.
[61]
Y. Q. Jiang, P. Zhang, S. C. Wong, and R. X. Liu, “A higher-order macroscopic model for pedestrian flows,” Physica A, vol. 389, no. 21, pp. 4623–4635, 2010.
[62]
Z. Markus, Electromagnetic Field Theory: A Problem Solving Approach, MIT OpenCourseWare, 2003.
[63]
R. S. Elliott, Electromagnetics: History, Theory, and Applications, Wiley-IEEE Press, 1999.
