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People Choice Modelling for Evacuation of Tall Buildings

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Abstract

Modelling people behaviour during emergencies has become an essential issue in attempting to increase safety aspects in buildings. This paper evaluates people’s choice behaviour for evacuation of tall buildings. A Stated Preference (SP) questionnaire was designed to understand underlying factors behind people behaviour and predict the likelihood of selecting evacuation lifts as opposed to stairs. Various scenarios including six different navigational cases, three levels for the density of people on stairs, three different number of people in the lift lobby and three vertical positions for refuge floors were administrated to 566 participants. A mixed logit model approach was then used to investigate how those factors influence the occupant’s decision-making as well as to capture the heterogeneity of different preferences among people. Traditionally, lifts were not allowed to be used in case of emergency, but the results indicate that people would tend to choose evacuation lifts in situations when they are suggested as the main exit option, and situations when stairs are overcrowded. Thus, if people are navigated by dynamic signs to use evacuation lifts, the percentage of lift users could go approximately from 70% to 80% for refuge floors between 15 and 55, respectively. In contrast, in situations when people have to make a decision between using lifts or stairs to evacuate, stairwells with fewer people as well as overcrowded refuge floors could lead to a decision in favour of stairs. This study represents the first SP experiment combining people decisions, pre-event opinions and beliefs related to evacuation lifts and stairs to understand their route choices for evacuation from tall buildings. The findings of this study can be used in the development of behavioural models for evacuation simulations of tall buildings.

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Notes

  1. Parameters of a model should be tested to determine the share of participants having either a positive or negative coefficient for a specific parameter. In situations when this share is pretty small, the share should be ignored and the coefficient should be fixed rather than random [18, 47].

References

  1. Tubbs J, Meacham B (2009) Selecting appropriate evacuation strategies for super tall buildings: current challenges and needs. In: Proceedings of the 4th international symposium on human behaviour in fire. Robinson College, Cambridge, pp 41–50

  2. Luo M, Wong KHL (2006) Evacuation strategy for super highrise buildings. In: 5th annual seminar tall building construction and maintenance. Hong Kong

  3. Bukowski RW (2008) Emergency egress from ultra tall buildings. In: Tall & green, typology for a sustainable urban future, CTBUH, Dubai, p 9

  4. Bukowski RW, Tubbs JS (2016) Egress concepts and design approaches BT. In: Hurley MJ, Gottuk DT, Hall Jr. JR, Harada K, Kuligowski ED, Puchovsky M, Torero JL, Watts Jr. JM, Wieczorek CJ (eds) SFPE handbook of fire protection engineering. Springer, New York, pp 2012–2046

    Chapter  Google Scholar 

  5. Ronchi E, Nilsson D (2013) Fire evacuation in high-rise buildings: a review of human behaviour and modelling research. Fire Sci Rev 2(1):1–21

    Article  Google Scholar 

  6. Kinateder MT, Omori H, Kuligowski ED (2014) The use of elevators for evacuation in fire emergencies in international buildings, pp 1–29

  7. Clawson K, O’Connor DJ (2011) Considerations and challenges for refuge areas in tall buildings. In: CTBUH 2011 world conference, pp 332–338

  8. Barber D, Johnson P (2015) Refuge floors—history, implementation and methods for improvement. In: Human behaviour in fire proceedings of the 6th international symposium, pp 245–256

  9. Ariff A (2003) Review of evacuation procedures for petronas twin towers. In: CIB-CTBUH international conference on tall buildings. CIB Publication no: 290, Kuala Lumpur

  10. Kuligowski ED (2016) Human behavior in fire. In: Hurley MJ, Gottuk DT, Hall JR, Harada K, Kuligowski ED, Puchovsky M, Torer JL, Watts JM, Wieczorek CJ (eds) SFPE handbook of fire protection engineering. Springer, New York, NY, pp 2070–2114

    Chapter  Google Scholar 

  11. Slovic P, Fischhoff B, Lichtenstein S (1977) Behavioral decision theory. Annu Rev Psychol 28(1):1–39

    Article  Google Scholar 

  12. Lovreglio R (2016) Modelling decision-making in fire evacuation using random utility theory. Politecnico di Bari, Milano

    Google Scholar 

  13. Zakay D (1993) The impact of time perception processes on decision making under time stress. In: Svenson O, Maule AJ (eds) Time pressure and stress in human judgment and decision making. Springer, Boston, MA, pp 59–72

  14. Ben Zur H, Breznitz SJ (1981) The effect of time pressure on risky choice behavior. Acta Psychol (Amst) 47(2):89–104

    Article  Google Scholar 

  15. Averill JD, Mileti DS, Peacock RD, Kuligowski ED, Groner N, Proulx G, Reneke AP (2005) Final report on the collapse of the World Trade Center towers. Federal building and fire safety investigation of the World Trade Center disaster, occupant behaviour. Egress and emergency communications. September. NIST NCSTAR 1–7. National Institute of St

  16. Lovreglio R (2016) Modelling decision-making in fire evacuation based on random utility theory. PhD Dissertation Politecnico of Bari, Milan and Turin

  17. Lovreglio R, Borri D, Ronchi E, Fonzone A, Dell’Olio L (2015) The need of latent variables for modelling decision-making in evacuation simulations. In: Proceedings of the IX international workshop on planning and evaluation, Bari

  18. Train K (2003) Discrete choice methods with simulation. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  19. McFadden D, Train K (2000) Mixed MNL models for discrete response. J Appl Econ 15(5):447–470

    Article  Google Scholar 

  20. Ben-Akiva M, Boccara B (1995) Discrete choice models with latent choice sets. Int J Res Mark 12(1):9–24

    Article  Google Scholar 

  21. Small KA, Rosen HS (1981) Applied welfare economics with discrete choice models. Econ J Econ Soc 49(1):105–130

    MathSciNet  MATH  Google Scholar 

  22. Ben-Akiva ME, Lerman SR (1985) Discrete choice analysis: theory and application to travel demand, vol 9. MIT Press, Cambridge

    Google Scholar 

  23. Bhat CR (2003) Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transp Res Part B Methodol 37(9):837–855

    Article  Google Scholar 

  24. Lovreglio R, Borri D, Dell’Olio L, Ibeas A (2014) A discrete choice model based on random utilities for exit choice in emergency evacuations. Saf Sci 62:418–426

    Article  Google Scholar 

  25. Lovreglio R, Fonzone A, Dell’Olio L (2016) A mixed logit model for predicting exit choice during building evacuations. Transp Res Part A 92:59–75

    Google Scholar 

  26. Lovreglio R, Fonzone A, Dell’Olio L, Borri D (2016) A study of herding behaviour in exit choice during emergencies based on random utility theory. Saf Sci 82:421–431

    Article  Google Scholar 

  27. Haghani M, Sarvi M, Shahhoseini Z (2015) Accommodating taste heterogeneity and desired substitution pattern in exit choices of pedestrian crowd evacuees using a mixed nested logit model. J Choice Model 16:58–68

    Article  Google Scholar 

  28. Haghani M, Sarvi M (2017) Stated and revealed exit choices of pedestrian crowd evacuees. Transp Res Part B Methodol 95:238–259

    Article  Google Scholar 

  29. Kinsey MJ, Galea ER, Lawrence PJ (2011) Stairs or lifts?—a study of human factors associated with lift/elevator usage during evacuations using an online survey. In: Pedestrian and evacuation dynamics. Springer, Boston, MA, pp 627–636

  30. Andrée K, Daniel N, Joakim E (2015) Evacuation experiments in a virtual reality high-rise building: exit choice and waiting time for evacuation elevators. Fire Mater 40(4):554–567

    Article  Google Scholar 

  31. Galea ER, Hulse L, Day R, Siddiqui A, Sharp G (2012) The UK WTC 9/11 evacuation study: an overview of findings derived from first‐hand interview data and computer modelling. Fire Mater 36(5–6):501–521

    Article  Google Scholar 

  32. Ding N, Chen T, Zhang H (2016) Experimental study of elevator loading and unloading time during evacuation in high-rise buildings. Fire Technol 53(1):1–14

    Google Scholar 

  33. Dell’Olio L, Ibeas A, Cecin P (2011) The quality of service desired by public transport users. Transp Policy 18(1):217–227

    Article  Google Scholar 

  34. Australian Building Codes Board (2013) Lifts used during evacuation, no. 2013

  35. NFPA (2012) NFPA 101 life safety code. National Fire Protection Association, Quincy

  36. Aloi S, Rogers J (2001) Evacuation and life safety strategies for super high rise buildings. In: Building for the 21st century: technology, livability, productivity, pp 429–436

  37. Gwynne SMV, Boyce KE, Kuligowski ED, Nilsson D, Robbins A, Lovrelgio R (2016) Pros and cons of egress drills. In: Interflam 2016, 14th international conference on fire science and engineering

  38. Notarianni KA, Parry GW (2016) Uncertainty. In: Hurley MJ, Gottuk DT, Hall JR, Harada K, Kuligowski ED, Puchovsky M, Torer JL, Watts JM, Wieczorek CJ (eds) SFPE handbook of fire protection engineering. Springer, New York, NY, pp 2992–3047

    Chapter  Google Scholar 

  39. McGuire WJ (1969) The nature of attitudes and attitude change. Handb Soc Psychol 3(2):136–314

    Google Scholar 

  40. Hovland CI, Janis IL, Kelley HH (1953) Communication and persuasion; psychological studies of opinion change. Yale University Press, New Haven

    Google Scholar 

  41. Pathfinder Thunderhead (2017) Technical reference

  42. Hanemann M, Loomis J, Kanninen B (1991) Statistical efficiency of double-bounded dichotomous choice contingent valuation. Am J Agric Econ 73(4):1255–1263

    Article  Google Scholar 

  43. Cai Y, Deilami I, Train K (1998) Customer retention in a competitive power market: analysis of a ‘double-bounded plus follow-ups’ questionnaire. Energy J, 19(2):191–215

    Google Scholar 

  44. Kinateder M (2013) Social influence in emergency situations–studies in virtual reality. Phd Dissertation University of Wuerzburg

  45. Kuligowski ED (2016) Computer evacuation models for buildings. In: SFPE handbook of fire protection engineering. Springer, pp 2152–2180

  46. Train K (2000) Halton sequences for mixed logit. Department of Economics, UCB, Berkeley

    Google Scholar 

  47. Train K, Croissant Y (2012) Kenneth Train’s exercises using the mlogit package for R. R 25:2

    Google Scholar 

  48. Neyman J, Pearson ES (1933) On the problem of the most efficient tests of statistical hypotheses. Biometrika A 20:175–240

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Infrastructure Engineering, Center for Disaster Management and Public Safety, The University of Melbourne, Walter Boas, 405, Parkville, VIC, 3010, Australia

    Mitko Aleksandrov, Abbas Rajabifard & Mohsen Kalantari

  2. School of Engineering and Advanced Technology, Massey University, Auckland, 0745, New Zealand

    Ruggiero Lovreglio

  3. Civil and Environmental Engineering, Faculty of Engineering, The University of Auckland, Auckland, 1010, New Zealand

    Vicente A. González

Authors
  1. Mitko Aleksandrov
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  2. Abbas Rajabifard
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  3. Mohsen Kalantari
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  4. Ruggiero Lovreglio
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  5. Vicente A. González
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Correspondence to Mitko Aleksandrov.

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Aleksandrov, M., Rajabifard, A., Kalantari, M. et al. People Choice Modelling for Evacuation of Tall Buildings. Fire Technol 54, 1171–1193 (2018). https://doi.org/10.1007/s10694-018-0731-1

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