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

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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.

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References

  • Aguirre B, Wenger D, Vigo G (1998) A test of the emergent norm theory of collective behavior. Sociol Forum 13:301–320

    Article  Google Scholar 

  • Aguirre BE, Torres MR, Gill KB, Hotchkiss HL (2011) Normative collective behavior in the station building fire. Soc Sci Quart 92(1):100–118

    Article  Google Scholar 

  • 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 87–97

    Google Scholar 

  • 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 Leeds

  • Choset HM (2005) Principles of robot motion. MIT Press, Cambridge

    MATH  Google Scholar 

  • Chu ML, Law KH (2013) A Computational Framework Incorporating Human Behaviors for Egress Simulations. ASCE J Comput Civil Eng 27(6):699–707

    Article  Google Scholar 

  • 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 35–42

  • Drury J, Cocking C, Reicher S (2009) Everyone for themselves? A comparative study of crowd solidarity among emergency survivors. Br J Soc Psychol 48:487–506

    Article  Google Scholar 

  • 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):22–31

    Article  Google Scholar 

  • 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 711–720

  • 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):55–64

    Google Scholar 

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

    Article  Google Scholar 

  • 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 439–444

  • 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):123–133

    Article  Google Scholar 

  • 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

  • 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, Boulder

  • Kuligowski ED, Peacock RD (2005) A review of building evacuation models. Technical Note 1471, Building and Fire Research Laboratory, NIST

  • Latombe J-C (1991) Robot motion planning. Kluwer, Boston

    Book  Google Scholar 

  • Lazer D et al (2009) Computational social science. Science 323:721–723

    Article  Google Scholar 

  • 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 347–357

  • Lindell MK, Perry RW (2011) The protective action decision model: theoretical modifications and additional evidence. Risk Anal 32(4):616–632

    Article  Google Scholar 

  • 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 245–268

    Google Scholar 

  • 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 62–67

  • Matsumura N, Fruchter R (2013) Shikake Trigger Categories. In: Proceedings of AAAI spring symposium on Shikakeology: designing triggers for behavior change, pp 68–73

  • Mawson AR (2005) Understanding mass panic and other collective responses to threat and disaster. Psychiatry 68:95–113

    Article  Google Scholar 

  • McPhail C (1991) The Myth of the Madding Crowd. Aldine de Gruyter, New York

    Google Scholar 

  • 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. 2011

  • Musse SR, Thalmann D (2001) Hierarchical model for real time simulation of virtual human crowds. IEEE Trans Vis Comput Graph 7:152–164

    Article  Google Scholar 

  • O’Neill MJ (1991) Effects of signage and floor plan configuration on wayfinding accuracy. Environ Behav 23(5):553–574

    Article  Google Scholar 

  • Pan X (2006) Computational modeling of human and social behavior for emergency egress analysis, Ph.D. Thesis, Stanford University

  • Reneke PA (2013) Evacuation decision model. NIST IR 7914, National Institute of Standards and Technology

  • Rosenberg RS, Baughman SL, Bailenson JN (2013) Virtual superheroes: using superpowers in virtual reality to encourage prosocial behavior. PLoS ONE 8(1):e55003

    Article  Google Scholar 

  • Rydgren J (2009) Beliefs. In: Hedström P, Bearman P (eds) The Oxford handbook of analytical sociology. Oxford University Press, Oxford, pp 72–93

    Google Scholar 

  • Salganik MJ, Dodds PS, Watts DJ (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science 311:854–856

    Article  Google Scholar 

  • Sime JD (1983) Affiliative behavior during escape to building exits. J Environ Psychol 3(1):21–41

    Article  Google Scholar 

  • 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 9:257–265

    Article  Google Scholar 

  • 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 457–464

  • Turner R, Killian L (1987) Collective Behavior, Englewood Cliffs, NJ: Prentice-Hall

    Google Scholar 

  • 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):473–490

    Article  Google Scholar 

  • 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/

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    Article  Google Scholar 

  • Zheng X, Zhong T, Liu M (2009) Modeling crowd evacuation of a building based on seven methodological approaches. Build Environ 44(3):437–445

    Article  Google Scholar 

Download references

Acknowledgments

This research is partially supported by the Center for Integrated Facility Engineering at Stanford University and a “Custom Research” grant through Stanford’s Center for Integrated Systems from NEC Corporation. The first author is also supported by the Croucher Foundation Scholarship and the Leavell Fellowship.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, 94305-4020, USA

    Mei Ling Chu & Kincho H. Law

  2. Sociology Department, Stanford University, Stanford, CA, 94305-4047, USA

    Paolo Parigi

  3. Kumagai Professor Emeritus, Computer Science Department, Stanford University, Stanford, CA, 94305-4010, USA

    Jean-Claude Latombe

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  1. Mei Ling Chu
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  2. Paolo Parigi
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  3. Jean-Claude Latombe
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  4. Kincho H. Law
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Correspondence to Mei Ling Chu.

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Chu, M.L., Parigi, P., Latombe, JC. et al. Simulating effects of signage, groups, and crowds on emergent evacuation patterns. AI & Soc 30, 493–507 (2015). https://doi.org/10.1007/s00146-014-0557-4

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  • DOI: https://doi.org/10.1007/s00146-014-0557-4

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