An escape route planning model based on wildfire prediction information and travel rate of firefighters
Junhao Sheng A , Xingdong Li A * , Xinyu Wang A , Yangwei Wang A , Sanping Li A , Dandan Li A , Shufa Sun B and Lijun Zhao C DA
B
C
D
Abstract
When firefighters evacuate from wildfires, escape routes are crucial safety measures, providing pre-defined pathways to a safety zone. Their key evaluation criterion is the time it takes for firefighters to travel along the planned escape routes.
While shorter travel times can help firefighters reach safety zones faster, this may expose them to the threat of wildfires. Therefore, the safety of the routes must be considered.
We introduced a new evaluation indicator called the safety index by predicting the growth trend of wildfires. We then proposed a comprehensive evaluation cost function as an escape route planning model, which includes two factors: (1) travel time; and (2) safety of the escape route. The relationship between the two factors is dynamically adjusted through real time factor. The safety window within real time factor provides ideal safety margins between firefighters and wildfires, ensuring the overall safety of escape routes.
Compared with other models, the escape routes planned by the final improved model not only effectively avoid wildfires, but also provide relatively short travel time and reliable safety.
This study ensures sufficient safety margins for firefighters escaping in wildfire environments.
The escape route model described in this study offers a broader perspective on the study of escape route planning.
Keywords: escape route, evacuation, firefighter safety, LANDFIRE, least-cost path modelling, topography, travel rates, wildland fire decision support system.
References
Alexander ME, Thomas DA (2004) Forecasting wildland fire behavior: Aids, guides, and knowledge-based protocols. Fire Management Today 64, 21-33.
| Google Scholar |
Alexander ME, Baxter GJ, Dakin GR (2005) Travel rates of Alberta wildland firefighters using escape routes. In ‘Eighth International Wildland Fire Safety Summit’, 26–28 April 2005, Missoula, MT, USA. (Eds BW Butler, ME Alexander) pp. 1–11. (International Association of Wildland Fire: Missoula, MT, USA)
Andrews PL (2014) Current status and future needs of the BehavePlus Fire Modeling System. International Journal of Wildland Fire 23, 21-33.
| Crossref | Google Scholar |
Beighley M (1995) Beyond the safety zone: creating a margin of safety. Fire Management Notes 55, 22-24.
| Google Scholar |
Butler BW, Cohen JD, Putnam T, Bartlette RA, Bradshaw LS (2000) A method for evaluating the effectiveness of firefighter escape routes. In ‘4th International Wildland Fire Safety Summit’, 10–12 October 2000, Edmonton, AB, Canada. (Eds BW Butler, KS Shannon) pp. 42–53. (International Association of Wildland Fire: Missoula, MT, USA)
Campbell MJ, Dennison PE, Butler BW (2017a) Safe separation distance score: a new metric for evaluating wildland firefighter safety zones using LiDAR. International Journal of Geographical Information Science 31, 1448-1466.
| Crossref | Google Scholar |
Campbell MJ, Dennison PE, Butler BW (2017b) A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping. International Journal of Wildland Fire 26, 884-895.
| Crossref | Google Scholar |
Campbell MJ, Dennison PE, Butler BW, Page WG (2019a) Using crowdsourced fitness tracker data to model the relationship between slope and travel rates. Applied Geography 106, 93-107.
| Crossref | Google Scholar |
Campbell MJ, Page WG, Dennison PE, Butler BW (2019b) Escape Route Index: A Spatially-Explicit Measure of Wildland Firefighter Egress Capacity. Fire 2, 40.
| Crossref | Google Scholar |
Chen A, Ding F, Zhou G, Zhou B (2022) Simulation Model of Forest Fire Spread Based on Swarm Intelligence. Journal of System Simulation 34, 1439.
| Crossref | Google Scholar |
Chowdhury EH, Hassan QK (2015) Development of a new daily-scale forest fire danger forecasting system using remote sensing data. Remote Sensing 7, 2431-2448.
| Crossref | Google Scholar |
Davey RC, Hayes M, Norman JM (1994) Running uphill: an experimental result and its applications. Journal of the Operational Research Society 45, 25-29.
| Crossref | Google Scholar |
Dennison PE, Fryer GK, Cova TJ (2014) Identification of firefighter safety zones using LiDAR. Environmental Modelling & Software 59, 91-97.
| Crossref | Google Scholar |
Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1, 269-271.
| Crossref | Google Scholar |
Fryer GK, Dennison PE, Cova TJ (2013) Wildland firefighter entrapment avoidance: modelling evacuation triggers. International Journal of Wildland Fire 22, 883-893.
| Crossref | Google Scholar |
Gleason P (1991) LCES – a key to safety in the wildland fire environment. Fire Management Notes 52, 9.
| Google Scholar |
Linn R, Reisner J, Colman , Winterkamp J (2002) Studying wildfire behavior using FIRETEC. International Journal of Wildland Fire 11, 233-246.
| Crossref | Google Scholar |
McLennan J, Holgate AM, Omodei MM, Wearing AJ (2006) Decision Making Effectiveness in Wildfire Incident Management Teams. Journal of Contingencies and Crisis Management 14, 27-37.
| Crossref | Google Scholar |
Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modelling grassland fires. International Journal of Wildland Fire 16, 1-22.
| Crossref | Google Scholar |
National Wildfire Coordinating Group (2016) Glossary A–Z. Available at http://www.nwcg.gov/glossary/a-z [verified 20 December 2016]
Norman JM (2004) Running uphill: energy needs and Naismith’s Rule. Journal of the Operational Research Society 55, 308-311.
| Crossref | Google Scholar |
Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18, 235-249.
| Crossref | Google Scholar |
Sullivan AL (2009) Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire 18, 369-386.
| Crossref | Google Scholar |
Sullivan PR, Campbell MJ, Dennison PE, Brewer SC, Butler BW (2020) Modeling Wildland Firefighter Travel Rates by Terrain Slope: Results from GPS-Tracking of Type 1 Crew Movement. Fire 3, 52.
| Crossref | Google Scholar |
Wen S, Zhang G, Wu X (2016) A decision-making study about multi-escaperoute network generating in forest fire. Journal of Central South University of Forestry & Technology 36, 62-65.
| Crossref | Google Scholar |
Wood NJ, Schmidtlein MC (2012) Anisotropic path modeling to assess pedestrian-evacuation potential from Cascadia-related tsunamis in the US Pacific Northwest. Natural Hazards 62, 275-300.
| Crossref | Google Scholar |
Zhong M, Fan W, Liu T, Li P (2003) Statistical analysis on current status of China forest fire safety. Fire Safety Journal 38, 257-269.
| Crossref | Google Scholar |
Ziegler JA (2007) The story behind an organizational list: a genealogy of wildland firefighters’ 10 standard fire orders. Communication Monographs 74, 415-442.
| Crossref | Google Scholar |