Fire Technology

, Volume 49, Issue 2, pp 411–431 | Cite as

Representation of the Impact of Smoke on Agent Walking Speeds in Evacuation Models

  • E. Ronchi
  • S. M. V. Gwynne
  • D. A. Purser
  • P. Colonna


This paper addresses the problem of reproducing the effect of different visibility conditions on people’s walking speed when using evacuation models. In particular, different strategies regarding the use of default settings and embedded data-sets are investigated. Currently, the correlation between smoke and walking speed is typically based on two different sets of experimental data produced by (1) Jin and (2) Frantzich and Nilsson. The two data-sets present different experimental conditions, but are often applied as if equivalent. In addition, models may implement the same data-sets in different ways. To test the impact of this representation within evacuation tools, the authors have employed six evacuation models, making different assumptions and employing different data-sets (FDS+EVAC, Gridflow, buildingEXODUS, STEPS, Pathfinder and Simulex). A simple case-study is simulated in order to investigate the sensitivity of the representation of two key variables: (1) initial occupant speeds in clear conditions, (2) extinction coefficients. Results show that (1) evacuation times appear to be consistent if models use the same data-sets and interpret the smoke vs speed correlation in the same manner (2) the same model may provide different results if applying different data-sets or interpretations for configuring the inputs; i.e. default settings are crucial for the calculation of the model results (3) models using embedded data-sets/assumptions require user expertise, experience and understanding to be employed appropriately and the results evaluated in a credible manner.


Evacuation modelling Human behaviour in fire Emergency evacuation Visibility Evacuation simulation 



The authors wish to thank Daniel Nilsson for providing the data-set of the tunnel experiments made by the Department of Fire Safety Engineering and Systems Safety at Lund University and for his valuable help in their interpretation. The authors wish also to acknowledge the model developers for providing their software and the support in their use.


  1. 1.
    Gwynne S, Galea ER, Lawrence PJ, Owen M, Filippidis L (1999) A review of the methodologies used in the computer simulation of evacuation from the built environment. Build Environ 34:741–749CrossRefGoogle Scholar
  2. 2.
    Kuligowski ED, Peacock RD, Hoskins BL (2010) A review of building evacuation models, 2nd edn. NIST Technical Note 1680, National Institute of Standards and Technology, GaithersburgGoogle Scholar
  3. 3.
    Santos G, Aguirre BE (2005) Critical review of emergency evacuation simulation models. In: Peacock RD, Kuligowski ED (eds) Workshop on building occupant movement during fire emergencies. National Institute of Standards and Technology, Gaithersburg, pp 27–52Google Scholar
  4. 4.
    Tavares RM (2009) Evacuation processes versus evacuation models: Quo Vadimus? Fire Technol 45:419–430. doi: 10.1007/s10694-008-0063-7 Google Scholar
  5. 5.
    Ronchi E, Kinsey M (2012) Evacuation models of the future. Insights from an online survey on user’s experiences and needs. In: Capote J et al. (eds) Advanced research workshop evacuation and human behaviour in emergency situations EVAC11, Santander, pp 145–155Google Scholar
  6. 6.
    Ronchi E, Alvear D, Berloco N, Capote J, Colonna P, Cuesta A (2012) The evaluation of different evacuation models for assessing road tunnel safety analysis. Tunn Undergr Space Technol 30:74–84. doi: 10.1016/j.tust.2012.02.008
  7. 7.
    Gwynne SMV, Kuligowski E (2010) The faults with default. In: Proceedings of the twelth international symposium INTERFLAM. Interscience Communications Ltd, London, pp 1473–1478Google Scholar
  8. 8.
    Ronchi E, Gwynne SMV, Purser DA (2011) The impact of default settings on evacuation model results: a study of visibility conditions vs occupant walking speeds. In Capote J et al. (eds) Advanced research workshop evacuation and human behaviour in emergency situations EVAC11, Santander, pp 81–95Google Scholar
  9. 9.
    Kuligowski ED (2011) Predicting human behavior during fires. Fire Technol. doi: 10.1007/s10694-011-0245-6.
  10. 10.
    Gwynne SMV, Rosenbaum ER (2008) Employing the hydraulic model in assessing emergency movement. In: DiNenno PJ, et al. (eds) The SFPE handbook of fire protection engineering, 4th edn. National Fire Protection Association, Quincy, MA, pp 3-396–3-373Google Scholar
  11. 11.
    Jin T (1976) Visibility through fire smoke (No. 42). Report of Fire Research Institute of JapanGoogle Scholar
  12. 12.
    Frantzich H, Nilsson D (2003) Utrymning genom tät rök: beteende och förflyttning, 75 p., Report 3126, Department of Fire Safety Engineering, Lund University, SwedenGoogle Scholar
  13. 13.
    Korhonen T, Hostikka S (2009) Fire dynamics simulator with evacuation: FDS+Evac technical reference and user’s guide, FDS 5.4.3, Evac 2.2.1Google Scholar
  14. 14.
    Bensilum M, Purser DA (2003) Gridflow: an object-oriented building evacuation model combining pre-movement and movement behaviours for performance-based design. Fire Saf Sci 7:941–952CrossRefGoogle Scholar
  15. 15.
    Galea ER, Gwynne S, Lawrence PJ, Filippidis L, Blackshields D, Cooney D (2004) buildingEXODUS V4.1 user guide and technical manual. University of GreenwichGoogle Scholar
  16. 16.
    Mott MacDonald Simulation Group (2011) Simulation of transient evacuation and pedestrian movements STEPS user manual, 4.1 versionGoogle Scholar
  17. 17.
    Thunderhead Engineering (2011) Pathfinder 2011. Technical ReferenceGoogle Scholar
  18. 18.
    Thompson PA, Marchant EW (1995) A computer model for the evacuation of large building populations. Fire Saf Sci. doi: 10.1016/0379-7112(95)00019-P Google Scholar
  19. 19.
    Bryan JL (2002) Behavioral response to fire and smoke. In DiNenno PJ et al. (eds) The SFPE handbook of fire protection engineering, 3rd edn. National Fire Protection Association, Quincy, pp 3-315–3-341.Google Scholar
  20. 20.
    Wright, MS, Cook, GK, Webber, GMB (2001). The effects of smoke on people’s walking Speeds using overhead lighting and Wayguidance provision. In: Proceedings of the 2nd international symposium on human behaviour in fire. MIT, Boston, pp 275–284, ISBN 0953231267.Google Scholar
  21. 21.
    Wood P (1972) The behaviour of people in fires. Fire Research Note No. 953, Fire Research StationGoogle Scholar
  22. 22.
    Xie H (2011) Investigation into the interaction of people with signage systems and its implementation within evacuation models. University of Greenwich, DissertationGoogle Scholar
  23. 23.
    Jin T (2008) Visibility and human behavior in fire smoke. In Di Nenno PJ et al. (eds) The SFPE handbook of fire protection engineering, 4th edn. NFPA, Quincy, MA, pp 2/54–2/66, ISBN 0-87765-821-8Google Scholar
  24. 24.
    Jin T, Yamada T (1990) Experimental study on human emotional instability in smoke filled corridor: part 2. J Fire Sci 1990(8):124. doi: 10.1177/073490419000800204 CrossRefGoogle Scholar
  25. 25.
    Purser DA (2009) Application of human behaviour and toxic hazard analysis to the validation of CFD modelling for the Mont Blanc Tunnel fire incident. In: Capote J et al. (eds) Fire protection and life safety in buildings and transportation systems proceedings, Santander, pp 23–57Google Scholar
  26. 26.
    Purser DA (2008) Assessment of hazards to occupants from smoke, toxic gases and heat. In: DiNenno PJ et al. (eds) SFPE handbook of fire protection engineering, 4th edn. National Fire Protection Association, Quincy, MAGoogle Scholar
  27. 27.
    McGrattan K, Hostikka S, Floyd J, Baum H, Rehm R, Mell W, McDermott R (2008) Fire dynamics simulator (version 5), technical reference guide. National Institute of Standards and Technology Special Publication 1018-5, Department of Commerce, Gaithersburg, MDGoogle Scholar
  28. 28.
    Zhang Q, Rubini PA (2011) Modelling of light extinction by soot particles. Fire Saf J 46:96–103. doi: 10.1016/j.firesaf.2010.11.00 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • E. Ronchi
    • 1
  • S. M. V. Gwynne
    • 2
  • D. A. Purser
    • 3
  • P. Colonna
    • 1
  1. 1.Department of Roads and TransportationPolytechnic University of Bari, ItalyBariItaly
  2. 2.Fire Safety Engineering GroupUniversity of GreenwichLondonUK
  3. 3.Hartford Environmental ResearchHatfieldUK

Personalised recommendations