Skip to main content

Setting Wildfire Evacuation Triggers by Coupling Fire and Traffic Simulation Models: A Spatiotemporal GIS Approach


Wildfire evacuation triggers refer to prominent geographic features used in wildfire evacuation practices, and when a fire crosses a feature, an evacuation warning is issued to the communities or firefighters in the path of the fire. The existing wildfire trigger modeling methods consider evacuation time as an input from a decision maker and employ fire spread modeling and GIS to create a trigger buffer around a threatened asset. This paper substantially improves on previous methods by coupling fire and traffic simulation models to set triggers, which allows us to estimate evacuation time using a traffic simulation model rather than relying on expert judgment. Specifically, we propose a three-step method within a spatiotemporal GIS framework to couple these models and to evaluate the value of the generated trigger buffers. The first step uses traffic simulation to estimate the total evacuation time for a threatened community. The second step derives the cumulative probabilities for distinct evacuation times from multiple simulations and generates corresponding probability-based trigger buffers. In the last step, we evaluate the value of the generated buffers by coupling fire and traffic simulation models to examine the spatial configurations of fire perimeters and evacuation traffic. A case study of Julian, California is used to test the proposed method. The results from two evacuation scenarios with different travel demand indicate that a larger trigger buffer (more lead time) will be needed for higher levels of evacuation travel demand. For example, the time required to guarantee that 95% of the evacuating residents arrive at the safe area as a fire approaches a community is estimated at 160 min for one scenario but 292 min if the travel demand is doubled. The resulting framework advances the dynamic representation of evacuation traffic in wildfires and improves our understanding of wildfire evacuation timing and decision making. The paper concludes with a discussion of the strengths and limitations of the proposed method, as well as future research directions.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12


  1. 1.

    Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase Western U.S. forest wildfire activity. Science 313(5789):940–943.

  2. 2.

    Dennison PE, Brewer SC, Arnold JD, Moritz MA (2014) Large wildfire trends in the western United States, 1984–2011. Geophys Res Lett 41(8):2928–2933.

    Article  Google Scholar 

  3. 3.

    Stewart SI, Radeloff VC, Hammer RB, Hawbaker TJ (2007) Defining the wildland-urban interface. J Forest 105(4):201–207

    Google Scholar 

  4. 4.

    Hammer RB, Stewart SI, Radeloff VC (2009) Demographic trends, the wildland–urban interface, and wildfire management. Soc Nat Resour 22(8):777–782.

    Article  Google Scholar 

  5. 5.

    Brenkert–Smith H, Champ PA, Flores N (2006) Insights into wildfire mitigation decisions among wildland–urban interface residents. Soc Nat Resour 19(8):759–768.

    Article  Google Scholar 

  6. 6.

    Cova TJ (2005) Public safety in the urban–wildland interface: should fire-prone communities have a maximum occupancy? Nat Hazards Rev 6(3):99–108.

    Article  Google Scholar 

  7. 7.

    Cova TJ, Theobald DM, Norman JB, Siebeneck LK (2013) Mapping wildfire evacuation vulnerability in the western US: the limits of infrastructure. GeoJournal 78(2):273–285.

    Article  Google Scholar 

  8. 8.

    Paveglio T, Carroll MS, Jakes PJ (2008) Alternatives to evacuation—protecting public safety during wildland fire. J Forest 106(2):65–70

    Google Scholar 

  9. 9.

    Cova TJ, Drews FA, Siebeneck LK, Musters A (2009) Protective actions in wildfires: evacuate or shelter-in-place? Nat Hazards Rev 10(4):151–162.

    Article  Google Scholar 

  10. 10.

    Tibbits A, Whittaker J (2007) Stay and defend or leave early: policy problems and experiences during the 2003 Victorian bushfires. Environ Hazards 7(4):283–290.

    Article  Google Scholar 

  11. 11.

    McNeill IM, Dunlop PD, Skinner TC, Morrison DL (2015) Predicting delay in residents’ decisions on defending v. evacuating through antecedents of decision avoidance. Int J Wildland Fire 24(2):153–161.

    Article  Google Scholar 

  12. 12.

    Drews FA, Musters A, Siebeneck LK, Cova TJ (2014) Environmental factors that influence wildfire protective-action recommendations. Int J Emerg Manag 10(2):153–168.

    Article  Google Scholar 

  13. 13.

    McCaffrey S, Wilson R, Konar A (2017) Should I stay or should I go now? Or should I wait and see? Influences on wildfire evacuation decisions. Risk Anal.

    Article  Google Scholar 

  14. 14.

    Handmer J, Tibbits A (2005) Is staying at home the safest option during bushfires? Historical evidence for an Australian approach. Glob Environ Change B Environ Hazards 6(2):81–91.

    Article  Google Scholar 

  15. 15.

    Lindell MK (2008) EMBLEM2: an empirically based large scale evacuation time estimate model. Trans Res A Policy Pract 42(1):140–154.

    MathSciNet  Article  Google Scholar 

  16. 16.

    Cook R (2003) Show Low, Arizona, inferno: evacuation lessons learned in the Rodeo–Chedeski fire. NFPA J 97(2):10–14

    Google Scholar 

  17. 17.

    Meyer JP (2012) Report: city was not slow to order Waldo Canyon evacuations. The Denver Post. Accessed 18 Feb 2018

  18. 18.

    Cova TJ, Dennison PE, Kim TH, Moritz MA (2005) Setting wildfire evacuation trigger points using fire spread modeling and GIS. Trans GIS 9(4):603–617.

    Article  Google Scholar 

  19. 19.

    Dennison PE, Cova TJ, Moritz MA (2007) WUIVAC: a wildland-urban interface evacuation trigger model applied in strategic wildfire scenarios. Nat Hazards 41(1):181–199.

    Article  Google Scholar 

  20. 20.

    Fryer GK, Dennison PE, Cova TJ (2013) Wildland firefighter entrapment avoidance: modelling evacuation triggers. Int J Wildland Fire 22(7):883–893.

    Article  Google Scholar 

  21. 21.

    Li D, Cova TJ, Dennison PE (2017) Using reverse geocoding to identify prominent wildfire evacuation trigger points. Appl Geogr 87:14–27.

    Article  Google Scholar 

  22. 22.

    Southworth F (1991) Regional evacuation modeling: a state-of-the-art review. Oak Ridge National Laboratory, Oak Ridge

    Book  Google Scholar 

  23. 23.

    Pel AJ, Bliemer MC, Hoogendoorn SP (2012) A review on travel behaviour modelling in dynamic traffic simulation models for evacuations. Transportation 39(1):97–123.

    Article  Google Scholar 

  24. 24.

    Wilmot C, Meduri N (2005) Methodology to establish hurricane evacuation zones. Transp Res Rec J Transp Res Board 1922:129–137.

    Article  Google Scholar 

  25. 25.

    Cova TJ, Johnson JP (2002) Microsimulation of neighborhood evacuations in the urban - wildland interface. Environ Plan A 34(12):2211–2229.

    Article  Google Scholar 

  26. 26.

    Lindell MK, Prater CS (2007) Critical behavioral assumptions in evacuation time estimate analysis for private vehicles: examples from hurricane research and planning. J Urban Plan Dev 133 (1):18–29.

    Article  Google Scholar 

  27. 27.

    Tweedie SW, Rowland JR, Walsh SJ, Rhoten RP, Hagle PI (1986) A methodology for estimating emergency evacuation times. Soc Sci J 23(2):189–204.

    Article  Google Scholar 

  28. 28.

    Chen X, Meaker JW, Zhan FB (2006) Agent-based modeling and analysis of hurricane evacuation procedures for the florida keys. Nat Hazards 38(3):321–338.

    Article  Google Scholar 

  29. 29.

    Chen X, Zhan FB (2008) Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies. J Oper Res Soc 59(1):25–33

    Article  MATH  Google Scholar 

  30. 30.

    Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest & Range Experiment Station, Ogden, UT

  31. 31.

    Finney MA (2006) An overview of FlamMap fire modeling capabilities. Fuels Management—How to Measure Success: Conference Proceedings. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO

  32. 32.

    Finney MA (1998) FARSITE: Fire Area Simulator—model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Ogden, UT

  33. 33.

    Van Wagner CE (1969) A simple fire-growth model. For Chron 45(2):103–104.

    MathSciNet  Article  Google Scholar 

  34. 34.

    Finney MA (2002) Fire growth using minimum travel time methods. Can J For Res 32(8):1420–1424.

    MathSciNet  Article  Google Scholar 

  35. 35.

    Clarke KC, Brass JA, Riggan PJ (1994) A cellular automation model of wildfire propagation and extinction. Photogramm Eng Remote Sensing 60(11):1355–1367

    Google Scholar 

  36. 36.

    Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271.

    MathSciNet  Article  MATH  Google Scholar 

  37. 37.

    Larsen JC, Dennison PE, Cova TJ, Jones C (2011) Evaluating dynamic wildfire evacuation trigger buffers using the 2003 Cedar Fire. Appl Geogr 31(1):12–19.

    Article  Google Scholar 

  38. 38.

    Li D, Cova TJ, Dennison PE (2015) A household-level approach to staging wildfire evacuation warnings using trigger modeling. Comput Environ Urban Syst 54:56–67.

    Article  Google Scholar 

  39. 39.

    Anguelova Z, Stow DA, Kaiser J, Dennison PE, Cova TJ (2010) Integrating fire behavior and pedestrian mobility models to assess potential risk to humans from wildfires within the U.S.–Mexico Border Zone. Prof Geogr 62(2):230–247.

    Article  Google Scholar 

  40. 40.

    Langran G, Chrisman NR (1988) A framework for temporal geographic information. Cartographica 25(3):1–14.

  41. 41.

    Peuquet D (2001) Making space for time: issues in space-time data representation. GeoInformatica 5(1):11–32.

    Article  MATH  Google Scholar 

  42. 42.

    Hägerstraand T (1970) What about people in regional science? Pap Reg Sci 24(1):7–24.

    Article  Google Scholar 

  43. 43.

    Miller HJ, Shaw S-L (2015) Geographic information systems for transportation in the 21st century. Geogr Compass 9(4):180–189.

    Article  Google Scholar 

  44. 44.

    Cova TJ, Dennison PE, Li D, Drews FA, Siebeneck LK, Lindell MK (2017) Warning triggers in environmental hazards: who should be warned to do what and when? Risk Anal 37(4):601–611.

    Article  Google Scholar 

  45. 45.

    Pultar E, Raubal M, Cova TJ, Goodchild MF (2009) Dynamic GIS case studies: wildfire evacuation and volunteered geographic information. Trans GIS 13:85–104.

    Article  Google Scholar 

  46. 46.

    Pultar E, Cova TJ, Yuan M, Goodchild MF (2010) EDGIS: a dynamic GIS based on space time points. Int J Geogr Inf Sci 24(3):329–346.

    Article  Google Scholar 

  47. 47.

    Yuan M (2001) Representing complex geographic phenomena in GIS. Cartogr Geogr Inf Sci 28(2):83–96.

    Article  Google Scholar 

  48. 48.

    Miller HJ, Wentz EA (2003) Representation and spatial analysis in geographic information systems. Ann Assoc Am Geogr 93(3):574–594.

    Article  Google Scholar 

  49. 49.

    Janelle DG (1969) Spatial reorganization: a model and concept. Ann Assoc Am Geogr 59(2):348–364.

    Article  Google Scholar 

  50. 50.

    Gatrell AC (1983) Distance and space: a geographical perspective. Oxford University Press, New York, NY

    Google Scholar 

  51. 51.

    Li D (2016) Modeling wildfire evacuation as a coupled human-environmental system using triggers. Dissertation, The University of Utah

  52. 52.

    Yuan M (1997) Use of knowledge acquisition to build wildfire representation in geographical information systems. Int J Geogr Inf Sci 11(8):723–746.

    Article  Google Scholar 

  53. 53.

    Wolshon B, Marchive E (2007) Emergency planning in the urban-wildland interface: subdivision-level analysis of wildfire evacuations. J Urban Plan Dev 133(1):73–81.

    Article  Google Scholar 

  54. 54.

    Han L, Yuan F, Urbanik T (2007) What is an effective evacuation operation? J Urban Plan Dev 133(1):3–8.

    Article  Google Scholar 

  55. 55.

    Beloglazov A, Almashor M, Abebe E, Richter J, Steer KCB (2016) Simulation of wildfire evacuation with dynamic factors and model composition. Simul Model Pract Theory 60:144–159.

    Article  Google Scholar 

  56. 56.

    Lämmel G, Grether D, Nagel K (2010) The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations. Trans Res C Emerg Technol 18(1):84–98.

    Article  Google Scholar 

  57. 57.

    Haklay M, Weber P (2008) Open street map: user-generated street maps. IEEE Pervasive Comput 7(4):12–18.

    Article  Google Scholar 

  58. 58.

    Goetz M, Zipf A (2012) Using crowdsourced geodata for agent-based indoor evacuation simulations. ISPRS Int J Geoinf 1(2):186

    Article  Google Scholar 

  59. 59.

    Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18(3):235–249.

    Article  Google Scholar 

  60. 60.

    Dennison PE, Moritz MA (2009) Critical live fuel moisture in chaparral ecosystems: a threshold for fire activity and its relationship to antecedent precipitation. Int J Wildland Fire 18(8):1021–1027.

    Article  Google Scholar 

  61. 61.

    Church RL, Cova TJ (2000) Mapping evacuation risk on transportation networks using a spatial optimization model. Trans Res C Emerg Technol 8(1–6):321–336.

    Article  Google Scholar 

  62. 62.

    Cova TJ, Church RL (1997) Modelling community evacuation vulnerability using GIS. Int J Geogr Inf Sci 11(8):763–784.

    Article  Google Scholar 

  63. 63.

    Murray-Tuite P, Wolshon B (2013) Evacuation transportation modeling: an overview of research, development, and practice. Trans Res C Emerg Technol 27:25–45.

    Article  Google Scholar 

  64. 64.

    Kobayashi T, Medina RM, Cova TJ (2011) Visualizing diurnal population change in urban areas for emergency management. Prof Geogr 63(1):113–130.

    Article  Google Scholar 

  65. 65.

    Bekhor S, Dobler C, Axhausen K (2011) Integration of activity-based and agent-based models. Transp Res Rec J Transp Res Board 2255:38–47.

    Article  Google Scholar 

  66. 66.

    Fu H, Wilmot C (2004) Sequential logit dynamic travel demand model for hurricane evacuation. Transp Res Rec J Transp Res Board 1882:19–26.

    Article  Google Scholar 

  67. 67.

    Arlikatti S, Lindell MK, Prater CS, Zhang Y (2006) Risk area accuracy and hurricane evacuation expectations of coastal residents. Environ Behav 38(2):226–247.

    Article  Google Scholar 

  68. 68.

    Zhang Y, Prater CS, Lindell MK (2004) Risk area accuracy and evacuation from hurricane bret. Nat Hazards Rev 5(3):115–120.

    Article  Google Scholar 

  69. 69.

    Kim T, Cova T, Brunelle A (2006) Exploratory map animation for post-event analysis of wildfire protective action recommendations. Nat Hazards Rev 7(1):1–11.

    Article  Google Scholar 

  70. 70.

    Cova TJ, Dennison PE, Drews FA (2011) Modeling evacuate versus shelter-in-place decisions in wildfires. Sustainability 3(10):1662.

    Article  Google Scholar 

  71. 71.

    Lindell MK, Prater CS (2007) A hurricane evacuation management decision support system (EMDSS). Nat Hazards 40(3):627–634.

    Article  Google Scholar 

  72. 72.

    Reid CE, Brauer M, Johnston FH, Jerrett M, Balmes JR, Elliott CT (2016) Critical review of health impacts of wildfire smoke exposure. Environ Health Perspect 124(9):1334–1343.

    Article  Google Scholar 

Download references


This research was funded by National Science Foundation CMMI-IMEE grant number 1100890. We thank the anonymous reviewers for their comments and suggestions. Lastly, the support and resources from the Center for High Performance Computing at the University of Utah are also gratefully acknowledged.

Author information



Corresponding author

Correspondence to Dapeng Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1

Appendix 1

See Table 5.

Table 5 The Vehicle Occupancy Data of Julian in 2015 from ACS

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, D., Cova, T.J. & Dennison, P.E. Setting Wildfire Evacuation Triggers by Coupling Fire and Traffic Simulation Models: A Spatiotemporal GIS Approach. Fire Technol 55, 617–642 (2019).

Download citation


  • Wildfire evacuation
  • Trigger modeling
  • Wildfire simulation
  • Traffic simulation
  • Model coupling
  • GIS