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Escape Dynamics in Office Buildings: Using Molecular Dynamics to Quantify the Impact of Certain Aspects of Human Behavior During Emergency Evacuation

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Abstract

Emergency evacuation of office buildings, in the event of a life-threatening situation such as a fire incident, is a function of various factors ranging from architectural to socio-behavioral ones. We show how individual-based modeling approaches such as molecular dynamics can be coupled with behavioral responses in panic situations, in order to quantify in a systematic way the impact of human familiarity to the space, during emergency evacuation. The ultimate goal is to identify certain crucial design features that can help in engineering better evacuation plans.

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References

  1. Benthorn, L., & Frantzich, H. (1999). Managing evacuating people from facilities during a fire emergency. Facilities, 17, 325–330.

    Article  Google Scholar 

  2. Blue, V. J., & Adler, J. L. (2000). Cellular automata microsimulation of bidirectional pedestrian flows. Journal of the Transportation Research Board, 1678, 135–141.

    Article  Google Scholar 

  3. Burstedde, C., Klauck, K., Schadschneider, A., & Zittartz, J. (2001). Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A, 295, 507–525.

    Article  Google Scholar 

  4. Cagdas, G., & Saglamer, G. (1995). A simulation model to predict the emptying times of buildings. Architectural Science Review, 38, 9–19.

    Google Scholar 

  5. Chertkoff, J., & Kushigian, R. (1999). Don’t panic: The psychology of emergency egress and ingress. London: Praeger.

    Google Scholar 

  6. Cova, T. J., & Johnson, J. P. (2002). Micro simulation of neighbourhood evacuations in the urban-wildland interface. Environment and Planning, A, 34, 2211–2229.

    Article  Google Scholar 

  7. Daan, F., & Berend, S. (2002). Understanding molecular simulation: From algorithms to applications. San Diego: Academic.

    Google Scholar 

  8. Deere, S. J., Galea, E. R., & Lawrence, P. J. (2009). A systematic methodology to assess the impact of human factors in ship design. Applied Mathematical Modelling, 33, 867–883.

    Article  Google Scholar 

  9. Dehne, M., & Kruse, D. (2007). Design of escape routes by simulating evacuation dynamics in conjunction with a probabilistic safety concept. Berlin: Springer.

    Google Scholar 

  10. Dijkstra, J., Jessurun, A. J., & Timmermans, H. J. P. (2001). A multi-agent cellular automata model of pedestrian movement. In M. Schreckenberg & S. D. Sharma (Eds.), Pedestrian and evacuation dynamics (pp. 173–181). Berlin: Springer.

    Google Scholar 

  11. Drager, K. H., Lovas, G. G., & Wiklund, J. (1992). EVACSIM—A comprehensive evacuation simulation tool. Proceedings of the 1992 International Emergency Management and Engineering Conference, Florida, 101–108.

  12. Feinberg, W. E., & Johnson, N. R. (2000). Firescap: A computer simulation model of reaction to a fire alarm. Journal of Mathematical Sociology, 20(2–3), 247–269.

    Google Scholar 

  13. Frenkel, D., & Smit, B. (2002). Understanding molecular simulation: From algorithms to applications. San Diego: Academic.

    Google Scholar 

  14. Galea, E. R. (2001). Predicting evacuation and circulation in planes, trains, buildings and ships using the EXODUS software. Proceedings of Conference on Pedestrian and Evacuation Dynamics, Duisburg, Germany.

  15. Galea, E. R., & Galparsoro, J. M. P. (1994). EXODUS: An evacuation model for mass transport vehicles. Fire Safety Journal, 22, 341–366.

    Article  Google Scholar 

  16. Guo, D., Ren, B., & Wang, C. (2008). Integrated agent-based modeling with GIS for large scale emergency simulation. In Lecture Notes in Computer Science (pp. 618–625). Berlin: Springer.

  17. Helbing, D. (1994). A mathematical model for the behavior of individuals in a social field. Journal of Mathematical Sociology, 19(3), 189–219.

    Article  Google Scholar 

  18. Helbing, D. (1995). Theoretical foundation of macroscopic traffic models. Physica A, 219(3–4), 375–390.

    Article  Google Scholar 

  19. Helbing, D. (2001). Traffic and related self-driven many-particle systems. Reviews of Modern Physics, 73, 1067–1141.

    Article  Google Scholar 

  20. Helbing, D. (2003). Agent-based simulation of traffic jams, crowds, and supply networks: Reality, simulation, and design of intelligent infrastructures. Institute for Economics and Traffic, Dresden University of Technology, Germany.

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

    Article  CAS  Google Scholar 

  22. Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review, 51(5), 4282–4286.

    CAS  Google Scholar 

  23. Henderson, L. F. (1974). On the fluid mechanics of human crowd motion. Transportation Research, 8, 509–515.

    Article  Google Scholar 

  24. International Standard (ISO) (2006). ISO 14520-1: Gaseous fire-extinguishing systems—Physical properties and system design, Part 1: General requirements. Switzerland: ISO.

  25. Johnson, N. R. (1987). Panic and the breakdown of social order: Popular myth, social theory, empirical evidence. Sociological Focus, 20, 171–183.

    Google Scholar 

  26. Johnson, N. R., & Feinberg, W. E. (1997). The impact of exit instructions and number of exits in fire emergencies: A computer simulation investigation. Journal of Environmental Psychology, 17(2), 123–133.

    Article  Google Scholar 

  27. Johnson, N. R., Stemler, J. G., & Hunter, D. (1977). Crowd behavior as “Risky Shift”: A laboratory experiment. Sociometry, 40, 183–187.

    Article  Google Scholar 

  28. Ketchell, N., Cole, S. S., & Webber, D. M. (1993). The EGRESS code for human movement and behaviour in emergency evacuation. In R. A. Smith & J. F. Dickie (Eds.), Engineering for crowd safety (pp. 361–370). Amsterdam: Elsevier.

    Google Scholar 

  29. Kim, H., Park, J. H., & Lee, D. (2003). An experimental analysis on the mobility of evacuating passengers in ship with regard to listing and motion. Journal of Ship and Ocean Engineering, 35, 125–138.

    Google Scholar 

  30. Kim, H., Park, J. H., Lee, D., & Yang, Y. S. (2004). Establishing the methodologies for human evacuation simulation in marine accidents. Computers and Industrial Engineering, 46, 725–740.

    Article  Google Scholar 

  31. Kisko, T. M., & Francis, R. L. (1985). EVACNET+: A computer program to determine optimal building evacuation plans. Fire Safety Journal, 9, 211–220.

    Article  Google Scholar 

  32. Klüpfel, H., Meyer-König, M., Wahle, J., & Schreckenberg, M. (2000). Microscopic simulation of evacuation processes on passenger ships. In S. Bandini & T. Worsch (Eds.), 2000 (Theoretical and practical issues on cellular automata, pp. 63–71). Berlin: Springer.

    Google Scholar 

  33. Kwon, E., & Pitt, S. (2005). Evaluation of emergency evacuation strategies for downtown event traffic using a dynamic network model. Journal of the Transportation Research Board, 1922, 149–155.

    Article  Google Scholar 

  34. Lämmel, G., Rieser, M., Nagel, K., Taubenboöck, Strunz, G., Gozeberg, N., et al. (2008). Emergency preparedness in the case of a tsounami-evacuation analysis and traffic optimization for the Indonesian city of Padang. In Pedestrian and Evacuation Dynamics Proceedings of the 4th Int. Conference, Wuppertal, Springer, Berlin.

  35. Langley, A., Mintzberg, H., Pitcher, P., Posada, E., & Saint-Macary, J. (1995). Opening up decision making: The view from the black stool. Organization Science, 6, 260–279.

    Article  Google Scholar 

  36. Lighthill, M. J., & Whitham, G. B. (1955). On kinematic waves: II a theory of traffic flow on long crowded roads. Proceedings of the Royal Society, London, Series A, 229, 317–345.

    Article  Google Scholar 

  37. Lo, S. M., Fang, Z., Lin, P., & Zhi, G. S. (2004). An evacuation model: The SGEM package. Fire Safety Journal, 39(3), 169–190.

    Article  Google Scholar 

  38. Mintz, A. (1951). No-adaptive group behavior. Journal Abnormal and Social Psychology, 46, 150–159.

    Article  CAS  Google Scholar 

  39. Nagatani, T. (2002). The physics of traffic jams. Reports on progress in physics, 65, 1331–1386.

    Article  CAS  Google Scholar 

  40. Nagel, K., & Schreckenberg, M. (1992). A cellular automaton model for freeway traffic. Journal de Physique I, 2, 2221–2229.

    Article  Google Scholar 

  41. Nilsson, D., & Johansson, A. (2009). Social influence during the initial phase of a fire evacuation—Analysis of evacuation experiments in a cinema theatre. Fire Safety Journal, 44(1), 71–79.

    Article  Google Scholar 

  42. Nishidate, K., & Baba, M. (1996). Cellular automaton model for random walkers. Physical Review Letters, 77(9), 1675–1678.

    Article  CAS  Google Scholar 

  43. Oven, V. A., & Cakici, N. (2009). Modelling the evacuation of a high-rise office building in Istanbul. Fire Safety Journal, 44, 1–15.

    Article  Google Scholar 

  44. Pan, X., Han, C. S., Dauber, K., & Law, K. H. (2007). A multi-agent based framework for the simulation of human and social behavior during emergency evacuations. AI and Society, 22(2), 113–132.

    Article  Google Scholar 

  45. Parisi, D. R., & Dorso, C. O. (2005). Microscopic dynamics of pedestrian evacuation. Physica A, 354, 606–618.

    Article  Google Scholar 

  46. Phillips, W. F. (1979). A kinetic model for traffic flow with continuum implications. Transportation Planning and Technology, 5, 131–138.

    Article  Google Scholar 

  47. Prigogine, I., & Herman, R. (1971). Kinetic theory of vehicular traffic. New York: Elsevier.

    Google Scholar 

  48. Quarantelli, E. (1954). The nature and conditions of panic. The American Journal of Sociology, 60(3), 267–275.

    Article  Google Scholar 

  49. Radwan, E. A., Hobeika, A. G., & Sivasailam, D. (1985). A computer simulation model for rural network evacuation under natural disasters. ITE Journal, 55, 25–30.

    Google Scholar 

  50. Raney, B., Cetin, N., Völlmy, A., Vrtic, M., Axhausen, K., & Nagel, K. (2003). An agent-based microsimulation model of Swiss travel: First results. Networks and Spatial Economics, 3, 23–41.

    Article  Google Scholar 

  51. Rapaport, D. C. (2004). The art of molecular dynamics (2nd ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  52. Richards, P. I. (1956). Shock waves on the highway. Operations Research, 4, 42–51.

    Article  Google Scholar 

  53. Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., & Seyfried, A. (2008). Evacuation dynamics: Empirical results, modeling and applications. In R. A. Meyers (Ed.), Encyclopedia of complexity and system science. Berlin: Springer.

    Google Scholar 

  54. Shields, T. J., & Boyce, K. E. (2000). A study of evacuation from large retail stores. Fire Safety Journal, 35(1), 25–49.

    Article  Google Scholar 

  55. Sime, J. D. (1985). Movement toward the familiar: Person and place affiliation in a fire entrapment setting. Environment and Behavior, 17(6), 697–724.

    Article  Google Scholar 

  56. Sisiopiku, V. P. (2007). Application of traffic simulation modeling for improved emergency preparedness planning. Urban Planning and Development, 133, 51–60.

    Article  Google Scholar 

  57. Thiago, T. P. (2005). An approach for modeling human cognitive behavior in evacuation models. Fire Safety Journal, 40(22), 177–189.

    Google Scholar 

  58. Thompson, P. A., & Marchant, E. W. (1995). A computer model for the evacuation of large building populations. Fire Safety Journal, 24, 131–148.

    Article  Google Scholar 

  59. Vanem, E., & Skjong, R. (2005). Designing for safety in passenger ships utilizing advanced evacuation analyses—A risk based approach. Safety Science, 44, 111–135.

    Article  Google Scholar 

  60. Vassalos D., Kim H., Christiansen G., Majumder J. (2001). A mesoscopic model for passenger evacuation in a virtual ship-sea environment and performance-based evaluation. Pedestrian and Evacuation Dynamics, April 4–6, 2001, Duisburg.

  61. Watts, J. M. (2000). Rescuing truth from familiarity. Fire Technology, 36(1), 1–2.

    Article  Google Scholar 

  62. Yamamoto, K., Kokubo, S., & Nishinari, K. (2007). Simulation for pedestrian dynamics by real-coded cellular automata (RCA). Physica A, 379, 654–660.

    Article  Google Scholar 

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Acknowledgments

We wish to thank Dr. Nick Baker, Department of Architecture, University of Cambridge for his valuable suggestions. S. T. R. wishes to acknowledge support by the Cambridge, European Trust ESRC-MRC Interdisciplinary studentships and the A.G. Leventis Foundation. C. I. S. acknowledges partial support by the National Technical University of Athens through the Basic Research Program “Constantin Caratheodory.”

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Correspondence to Constantinos I. Siettos.

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Rassia, S.T., Siettos, C.I. Escape Dynamics in Office Buildings: Using Molecular Dynamics to Quantify the Impact of Certain Aspects of Human Behavior During Emergency Evacuation. Environ Model Assess 15, 411–418 (2010). https://doi.org/10.1007/s10666-009-9209-3

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