AI & SOCIETY

, Volume 30, Issue 4, pp 493–507 | Cite as

Simulating effects of signage, groups, and crowds on emergent evacuation patterns

  • Mei Ling Chu
  • Paolo Parigi
  • Jean-Claude Latombe
  • Kincho H. Law
Original Article

Abstract

Studies of past emergency events have revealed that occupants’ behaviors, egress signage system, local geometry, and environmental constraints affect crowd movement and govern the building evacuation. In addition to complying with code and standards, building designers need to consider the occupants’ social characteristics and the unique layout of the buildings to design occupant-centric egress systems. This paper describes an agent-based egress simulation tool, SAFEgress, which incorporates important human and social behaviors observed by researchers in safety and disaster management. Agents in SAFEgress are capable of perceiving building emergency features in the virtual environment and deciding their behaviors and navigation. In particular, we describe four agent behavioral models, namely following familiar exits, following cues from building features, navigating with social groups, and following crowds. We use SAFEgress to study how agents (mimicking building occupants) react to different signage arrangements in a modeled environment. We explore agents’ reactions to cues as an emergent phenomenon, shaped by the interactions among groups and crowds. Simulation results from the prototype reveal that different designs of building emergency features and levels of group interactions can trigger different crowd flow patterns and affect overall egress performance. By considering the occupants’ perception about the emergency features using the SAFEgress prototype, engineers, designers, and facility managers can study the human factors that may influence an egress situation and, thereby, improve the design of SAFEgress systems and procedures.

Keywords

Crowd simulation Egress simulation Building egress Social agents Social behavior Collective behavior Simulated perception 

References

  1. Aguirre B, Wenger D, Vigo G (1998) A test of the emergent norm theory of collective behavior. Sociol Forum 13:301–320CrossRefGoogle Scholar
  2. Aguirre BE, Torres MR, Gill KB, Hotchkiss HL (2011) Normative collective behavior in the station building fire. Soc Sci Quart 92(1):100–118CrossRefGoogle Scholar
  3. Burstedde C, Kirchner A, Klauck K, Schadschneider A, Zittartz J (2001) Cellular automaton approach to pedestrian dynamics—applications. Pedestrian and Evacuation Dynamics. Springer, Berlin, pp 87–97Google Scholar
  4. Challenger W, Clegg WC, Robinson AM (2009) Understanding crowd behaviors: Guidance and lessons identified. Technical Report prepared for UK Cabinet Office, Emergency Planning College, University of LeedsGoogle Scholar
  5. Choset HM (2005) Principles of robot motion. MIT Press, CambridgeMATHGoogle Scholar
  6. Chu ML, Law KH (2013) A Computational Framework Incorporating Human Behaviors for Egress Simulations. ASCE J Comput Civil Eng 27(6):699–707CrossRefGoogle Scholar
  7. Chu ML, Parigi P, Law KH, Latombe J-C (2014). SAFEgress: a flexible platform to study the effect of human and social behaviors on egress performance. Proceedings of 2014 Symposium on Simulation for Architecture and Urban Design, pp 35–42Google Scholar
  8. Drury J, Cocking C, Reicher S (2009) Everyone for themselves? A comparative study of crowd solidarity among emergency survivors. Br J Soc Psychol 48:487–506CrossRefGoogle Scholar
  9. Durupinar F, Pelechano N, Allbeck J, Gudukbay U, Badler NI (2011) How the OCEAN personality model affects the perception of crowds. IEEE Comput Graph Appl 31(3):22–31CrossRefGoogle Scholar
  10. Galea ER, Gwynne S, Owen M, Lawrence PJ, Filipidis L (1998) A comparison of predictions from the buildingEXODUS evacuation model with experimental data. Proceedings of the first international symposium on human behavior in fire, pp 711–720Google Scholar
  11. Gärling T, Böök A, Lindberg E (1986) Spatial orientation and wayfinding in the designed environment: a conceptual analysis and some suggestions for post-occupancy evaluation. J Archit Plan Res 3(1):55–64Google Scholar
  12. Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407:487–490CrossRefGoogle Scholar
  13. Hoogendoorn M, Treur J, Van der Wal C, Wissen AV (2010) An agent-based model for the interplay of information and emotion in social diffusion. Proceedings of web intelligence and intelligent agent technology (WI-IAT), pp 439–444Google Scholar
  14. Johnson NR, Feinberg WE (1997) The impact of exit instructions and number of exits in fire emergencies: a computer simulation investigation. J Environ Psychol 17(2):123–133CrossRefGoogle Scholar
  15. Kneidl A, Hartmann D, Borrmann A (2013) A hybrid multi-scale approach for simulation of pedestrian dynamics. Trans Res Part C Emerg Technol. http://www.sciencedirect.com/science/article/pii/S0968090X13000594
  16. Kuligowski ED (2011) Terror defeated: occupant sensemaking, decision-making and protective action in the 2001 World Trade Center disaster, Ph.D. Thesis, University of Colorado, BoulderGoogle Scholar
  17. Kuligowski ED, Peacock RD (2005) A review of building evacuation models. Technical Note 1471, Building and Fire Research Laboratory, NISTGoogle Scholar
  18. Latombe J-C (1991) Robot motion planning. Kluwer, BostonCrossRefGoogle Scholar
  19. Lazer D et al (2009) Computational social science. Science 323:721–723CrossRefGoogle Scholar
  20. Lin Y, Fedchenia I, LaBarre B, Tomastik R (2010). Agent-based simulation of evacuation: an office building case study. Pedestrian and Evacuation Dynamics 2008. Springer, Berlin pp 347–357Google Scholar
  21. Lindell MK, Perry RW (2011) The protective action decision model: theoretical modifications and additional evidence. Risk Anal 32(4):616–632CrossRefGoogle Scholar
  22. Macy M, Flache A (2009) Social dynamics from the bottom up: Agent-based models of social interaction. In: Hedström P, Bearman P (eds) The Oxford handbook of analytical sociology. Oxford University Press, Oxford, pp 245–268Google Scholar
  23. Matsumura N (2013) Shikake as an embodied trigger for behavior change. In: Proceedings of AAAI Spring Symposium on Shikakeology: designing triggers for behavior change, pp 62–67Google Scholar
  24. Matsumura N, Fruchter R (2013) Shikake Trigger Categories. In: Proceedings of AAAI spring symposium on Shikakeology: designing triggers for behavior change, pp 68–73Google Scholar
  25. Mawson AR (2005) Understanding mass panic and other collective responses to threat and disaster. Psychiatry 68:95–113CrossRefGoogle Scholar
  26. McPhail C (1991) The Myth of the Madding Crowd. Aldine de Gruyter, New YorkGoogle Scholar
  27. Moussaïd M, Helbing D, Theraulaz G (2011) How simple rules determine pedestrian behavior and crowd disasters. In: Proceedings of the National Academy of Sciences of the United States of America, Apr. 2011Google Scholar
  28. Musse SR, Thalmann D (2001) Hierarchical model for real time simulation of virtual human crowds. IEEE Trans Vis Comput Graph 7:152–164CrossRefGoogle Scholar
  29. O’Neill MJ (1991) Effects of signage and floor plan configuration on wayfinding accuracy. Environ Behav 23(5):553–574CrossRefGoogle Scholar
  30. Pan X (2006) Computational modeling of human and social behavior for emergency egress analysis, Ph.D. Thesis, Stanford UniversityGoogle Scholar
  31. Reneke PA (2013) Evacuation decision model. NIST IR 7914, National Institute of Standards and TechnologyGoogle Scholar
  32. Rosenberg RS, Baughman SL, Bailenson JN (2013) Virtual superheroes: using superpowers in virtual reality to encourage prosocial behavior. PLoS ONE 8(1):e55003CrossRefGoogle Scholar
  33. Rydgren J (2009) Beliefs. In: Hedström P, Bearman P (eds) The Oxford handbook of analytical sociology. Oxford University Press, Oxford, pp 72–93Google Scholar
  34. Salganik MJ, Dodds PS, Watts DJ (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science 311:854–856CrossRefGoogle Scholar
  35. Sime JD (1983) Affiliative behavior during escape to building exits. J Environ Psychol 3(1):21–41CrossRefGoogle Scholar
  36. Tong D, Canter D (1985) The decision to evacuate: a study of the motivations which contribute to evacuation in the event of a fire. Fire Saf J 9:257–265CrossRefGoogle Scholar
  37. Tsai J, Fridman N, Bowring E, Brown M, Epstein S, Kaminka G, Marsella S, Ogden A, Rika I, Sheel A, Taylor ME, Wang X, Zilka A, Tambe M (2011) ESCAPES: Evacuation simulation with children, authorities, parents, emotions, and social comparison. In: Proceedings of the 10th international conference on autonomous agents and multiagent systems, ACM Press, pp 457–464Google Scholar
  38. Turner R, Killian L (1987) Collective Behavior, Englewood Cliffs, NJ: Prentice-HallGoogle Scholar
  39. Turner A, Penn A (2002) Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment. Environ Plan 29(4):473–490CrossRefGoogle Scholar
  40. Veeraswamy A, Lawrence P, Galea E (2009) Implementation of cognitive mapping, spatial representation and way finding behaviours of people within evacuation modelling tools. 2009 Human Behavior in Fire Symposium. http://gala.gre.ac.uk/1297/
  41. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442CrossRefGoogle Scholar
  42. Zheng X, Zhong T, Liu M (2009) Modeling crowd evacuation of a building based on seven methodological approaches. Build Environ 44(3):437–445CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Mei Ling Chu
    • 1
  • Paolo Parigi
    • 2
  • Jean-Claude Latombe
    • 3
  • Kincho H. Law
    • 1
  1. 1.Department of Civil and Environmental EngineeringStanford UniversityStanfordUSA
  2. 2.Sociology DepartmentStanford UniversityStanfordUSA
  3. 3.Kumagai Professor Emeritus, Computer Science DepartmentStanford UniversityStanfordUSA

Personalised recommendations