Abstract
The pre-movement time plays an important role in the calculation of Required Safe Egress Time (RSET). The effect of pre-movement time on evacuation is of great importance to the analysis of occupants’ safety in fire egress. In this paper, the evacuation software (FDS+Evac) was used to explore the effect of a mean pre-movement time characterized by Weibull distribution on the evacuation time in a room with one exit. The variation of density and velocity was also considered. The results with Weibull distributed pre-movement time and those with constant pre-movement time were compared. The analysis suggested the following: (1) with the decreasing mean pre-movement time characterized by Weibull distribution, the rate of change for evacuation time is more likely to tend toward constant as a result of queuing time at the exit, and (2) the pre-movement time characterized by Weibull distribution has an advantage compared to a constant value in reality. The pre-movement time characterized by Weibull distribution reflects people’s characteristics and distributed response time in a real fire, and the results in this paper shows the Weibull distributed pre-movement time compared to a constant pre-movement time affects the overall evacuation time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- ASET:
-
Available safe egress time (s)
- fi :
-
The final force (N)
- m:
-
Mass of person (kg)
- RSET:
-
Required safe egress time (s)
- ta :
-
The alarm time (s)
- td :
-
The detection time (s)
- tm :
-
Movement time (s)
- tp :
-
The pre-movement time (s)
- v:
-
Velocity (m/s)
References
Seyfried A, Steffen B, Klingsch W et al (2007) The fundamental diagram of occupant movement revisited – empirical results and modelling. Traffic Granul Flow 05:305–314
Lv W, Song WG, Ma J et al (2013) A two-dimensional optimal velocity model for unidirectional occupant flow based on occupant’s visual hindrance field. IEEE Trans Intell Transp Syst 14(4):1753–1763
Nagatani T, Nagai R (2004) Statistical characteristics of evacuation without visibility in random walk model. Phys A: Stat Mech Appl 341:638–648
Isobe M, Helbing D, Nagatani T (2004) Experiment, theory, and simulation of the evacuation of a room without visibility. Phys Rev E 69(6):066132
Pereira LA, Duczmal LH, Cruz FRB (2013) Congested emergency evacuation of a population using a finite automata approach. Saf Sci 51(1):267–272
Shields TJ, Silcock GW, Dunlop KE (1992) A methodology for the determination of code equivalency with respect to the provision of means of escape. Fire Saf J 19(4):267–278
Kong DP, Johansson N, van Hees P et al (2013) A Monte Carlo analysis of the effect of heat release rate uncertainty on available safe egress time. J Fire Protect Eng 23(1):5–29
Chu GQ, Sun JH (2006) The effect of pre-movement time and occupant density on evacuation time. J Fire Sci 24(3):237–259
Kuligowski E (2013) Predicting human behavior during fires. Fire Technol 49(1):101–120
Gwynne S, Galea ER, Owen M et al (1999) A review of the methodologies used in the computer simulation of evacuation from the built environment. Build Environ 34(6):741–749
Purser DA, Bensilum M (2001) Quantification of behaviour for engineering design standards and escape time calculations. Saf Sci 38(2):157–182
Vistnes J, Grubits SJ, He Y (2005) A stochastic approach to occupant pre-movement in fires. Fire Saf Sci 8:531–542
MacLennan HA, Regan MA, Ware R (1999) An engineering model for the estimation of occupant premovement and or response times and the probability of their occurrence. Fire and Mate 23(6): 255–263
Chu GQ, Sun JH, Wang QS et al (2006) Simulation study on the effect of pre-evacuation time and exit width on evacuation. Chin Sci Bull 51(11):1381–1388
Hostikka TKaS (2010) Fire dynamics simulator with evacuation FDS+Evac, VTT Technical Research Centre of Finland
Moussaid M, Helbing D, Garnier S et al (2009) Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proc R Soc B-Biol Sci 276(1668):2755–2762
Moussaid M, Helbing D, Theraulaz G (2011) How simple rules determine occupant behavior and crowd disasters. Proc Natl Acad Sci USA 108(17):6884–6888
Johansson A, Helbing D (2008) From crowd dynamics to crowd safety: a video-based analysis. Adv Compl Syst 11(4):497–527
Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407(6803):487–490
Helbing D, Farkas IJ, Molnar P et al (2002) Simulation of occupant crowds in normal and evacuation situations. Occupant Evacuation Dyn 21:21–58
Werner T, Helbing D (2003) The social force occupant model applied to real life scenarios. Occupant Evacuation Dyn 17–26
Acknowledgment
The study is supported by the National Basic Research Program of China (No.2012CB719705), National Natural Science Foundation of China (No.51178445, 51120165001, and 51323010), Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20133402110009), the Fundamental Research Funds for the Central Universities (WK2320000014), National Natural Science Foundation of China (51323010), and the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 51308526).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Zeng, Y., Song, W., Huo, F., Wei, X. (2017). Effect of Weibull Distributed Pre-movement Time on Evacuation. In: Harada, K., Matsuyama, K., Himoto, K., Nakamura, Y., Wakatsuki, K. (eds) Fire Science and Technology 2015. Springer, Singapore. https://doi.org/10.1007/978-981-10-0376-9_13
Download citation
DOI: https://doi.org/10.1007/978-981-10-0376-9_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0375-2
Online ISBN: 978-981-10-0376-9
eBook Packages: EngineeringEngineering (R0)