Efficient evacuation of wildfire-threatened communities is a pressing challenge. A reliable evacuation planning and execution requires a comprehensive understanding of the diverse and interdependent physical, social, and behavioral components, and advanced, yet easy to use decision support system. This paper proposes the Wildfire Safe Egress (WiSE) framework, which integrates the fire dynamics, human behavior, and traffic model to predict the chance of safe egress by any given community during a wildfire evacuation. WISE framework presents a unified dependency diagram and workflow offering consistent granularity between sub-models and creates comparable evacuation scenarios. A human behavior model is proposed to predict the community decision making and action based on their socio-demographic vulnerability profile. An agent-based stochastic approach generates evacuation departure times. The travel times are calculated through a congestion-informed traffic simulation. Finally, a Bayesian Network is used to combine the sub-models and to predict community safety (probability of successful evacuation) via probabilistic inference based on the integrated model. A proof-of-concept software implementation of the WiSE framework is also presented. To demonstrate the model and platform capabilities the evacuation of the entire city of Paradise during the California Camp Fire 2018 is simulated. The simulation results are qualitatively validated by the firefighters who served in this disaster. A sensitivity analysis of the parameters is performed to compare several evacuation scenarios and provide insights for future wildfire evacuation plannings.
KramerHAMockrinMHAlexandrePM, et al. High wildfire damage in interface communities in California. Int J Wildland Fire2019; 28: 641–650.
2.
GodoyMMMartinuzziSKramerHA, et al. Rapid WUI growth in a natural amenity-rich region in central-western Patagonia, Argentina. Int J Wildland Fire2019; 28: 473–484.
3.
KetchpawARLiDKhanSN, et al. Using structure location data to map the Wildland–Urban interface in Montana, USA. Fire2022; 5: 129.
4.
LiuZWimberlyMCLamsalA, et al. Climate change and wildfire risk in an expanding wildland–urban interface: a case study from the Colorado Front Range Corridor. Landsc Ecol2015; 30: 1943–1957.
5.
MockrinMHHelmersDMartinuzziS, et al. Growth of the wildland-urban interface within and around U.S. National Forests and Grasslands, 1990–2010. Landsc Urban Plan2022; 218: 104283.
6.
RadeloffVCHelmersDPKramerHA, et al. Rapid growth of the US wildland-urban interface raises wildfire risk. Proc Natl Acad Sci USA2018; 115: 3314–3319.
KhanmohammadiSArashpourMGolafshaniEM, et al. Prediction of wildfire rate of spread in grasslands using machine learning methods. Environ Model Softw2022; 156: 105507.
10.
DehkordiHR. Applications of Randers geodesics for wildfire spread modelling. Appl Math Model2022; 106: 45–59.
11.
StoreyMABedwardMPriceOF, et al. Derivation of a Bayesian fire spread model using large-scale wildfire observations. Environ Model Softw2021; 144: 105127.
12.
MasoudvaziriNSzasdi BardalesFKeskinOK, et al. Streamlined wildland-urban interface fire tracing (SWUIFT): Modeling wildfire spread in communities. Environ Model Softw2021; 143: 105097.
13.
ZhaiCZhangSCaoZ, et al. Learning-based prediction of wildfire spread with real-time rate of spread measurement. Combust Flame2020; 215: 333–341.
14.
KhakzadN. Modeling wildfire spread in wildland-industrial interfaces using dynamic Bayesian network. Reliab Eng Syst Saf2019; 189: 165–176.
15.
WongSDBroaderJCShaheenSA. Can sharing economy platforms increase social equity for vulnerable populations in disaster response and relief? A case study of the 2017 and 2018 California Wildfires. Transp Res Interdiscip Perspect2020; 5: 100131.
16.
AndersenLMSuggMM. Geographic multi-criteria evaluation and validation: A case study of wildfire vulnerability in Western North Carolina, USA following the 2016 wildfires. Int J Disaster Risk Reduct2019; 39: 101123.
17.
PalaiologouPAgerAANielsen-PincusM, et al. Social vulnerability to large wildfires in the western USA. Landsc Urban Plan2019; 189: 99–116.
18.
PaveglioTBEdgeleyCMStasiewiczAM. Assessing influences on social vulnerability to wildfire using surveys, spatial data and wildfire simulations. J Environ Manage2018; 213: 425–439.
19.
BasavarajVVShekarVFiondellaL, et al. Algorithm to prioritize the restoration of a multiple facility, multiple-hazard road network. Proc IMechE, Part O: J Risk and Reliability2017; 231: 221–231.
20.
FangCDongPFangY-P, et al. Vulnerability analysis of critical infrastructure under disruptions: An application to China Railway High-speed. Proc IMechE, Part O: J Risk and Reliability2020; 234: 235–245.
21.
OstadiBSeifiMMHusseinzadeh KashanA. A multi-objective model for resource allocation in disaster situations to enhance the organizational resilience and maximize the value of business continuity with considering events interactions. Proc IMechE, Part O: J Risk and Reliability2021; 235: 814–830.
22.
MuhsJWParvaniaMShahidehpourM. Wildfire risk mitigation: a paradigm shift in power systems planning and operation. IEEE Open Access J Power Energy2020; 7: 366–375.
23.
BakerGWebbAWhitingP. Regulatory controls for buildings in wildfire-prone areas of Australia. Fire Technol2020; 56: 1903–1935.
24.
BakerWL. Defensible-space treatment of < 114,000 ha 40 m from high-risk buildings near wildland vegetation could reduce loss in WUI wildfire disasters across Colorado’s 27 million ha. Landsc Ecol2022; 37: 2967–2976.
25.
CaggianoMDHawbakerTJGannonBM, et al. Building loss in WUI disasters: Evaluating the core components of the wildland–urban interface definition. Fire2020; 3: 73.
26.
WahlqvistJRonchiEGwynneSMV, et al. The simulation of wildland-urban interface fire evacuation: The WUI-NITY platform. Saf Sci2021; 136: 105145.
27.
RonchiEGwynneSMVReinG, et al. An open multi-physics framework for modelling wildland-urban interface fire evacuations. Saf Sci2019; 118: 868–880.
28.
SchröderBArnoldLSeyfriedA. A map representation of the ASET-RSET concept. Fire Saf J2020; 115: 103154.
29.
GrajduraSQianXNiemeierD. Awareness, departure, and preparation time in no-notice wildfire evacuations. Saf Sci2021; 139: 105258.
30.
ChenYShafiSYChenY. Simulation pipeline for traffic evacuation in urban areas and emergency traffic management policy improvements through case studies. Transp Res Interdiscip Perspect2020; 7: 100210.
31.
MontecchiariGBulianGGallinaP. Towards real-time human participation in virtual evacuation through a validated simulation tool. Proc IMechE, Part O: J Risk and Reliability2018; 232: 476–490.
32.
Grand BlancMCarpignanoADullaS, et al. A cellular automaton model for offshore evacuation risk assessment. Proc IMechE, Part O: J Risk and Reliability2017; 231: 138–145.
33.
ZouQZhangTLiuW. A fire risk assessment method based on the combination of quantified safety checklist and structure entropy weight for shopping malls. Proc IMechE, Part O: J Risk and Reliability2021; 235: 610–626.
34.
McLennanJRyanBBearmanC, et al. Should we leave now? Behavioral factors in evacuation under wildfire threat. Fire Technol2019; 55: 487–516.
35.
SiamMRKWangHLindellMK, et al. An interdisciplinary agent-based multimodal wildfire evacuation model: Critical decisions and life safety. Transp Res Part Transp Environ2022; 103: 103147.
36.
Ruiz-TagleALewisADSchellCA, et al. BaNTERA: a Bayesian network for third-party excavation risk assessment. Reliab Eng Syst Saf2022; 223: 108507.
37.
StasiewiczAMPaveglioTB. Preparing for wildfire evacuation and alternatives: Exploring influences on residents’ intended evacuation behaviors and mitigations. Int J Disaster Risk Reduct2021; 58: 102177.
38.
MolanSWeberDKorM. ‘Why can’t they just evacuate early’: a study of the effect of a virtual reality experience on residents’ intentions related to a wildfire risk. Int J Disaster Risk Reduct2022; 81: 103268.
39.
GrajduraSBorjiginSNiemeierD. Fast-moving dire wildfire evacuation simulation. Transp Res Part Transp Environ2022; 104: 103190.
40.
KatzilierisKVlahogianniEIWangH. Evacuation behavior of affected individuals and households in response to the 2018 Attica wildfires: from empirical data to models. Saf Sci2022; 153: 105799.
41.
VaiciulyteSHulseLMVeeraswamyA, et al. Cross-cultural comparison of behavioural itinerary actions and times in wildfire evacuations. Saf Sci2021; 135: 105122.
42.
McGeeTKNationMOChristiansonAC. Residents’ wildfire evacuation actions in Mishkeegogamang Ojibway Nation, Ontario, Canada. Int J Disaster Risk Reduct2019; 33: 266–274.
43.
ZhaoXXuYLovreglioR, et al. Estimating wildfire evacuation decision and departure timing using large-scale GPS data. Transp Res Part Transp Environ2022; 107: 103277.
44.
LiLMaZCaoT. Data-driven investigations of using social media to aid evacuations amid Western United States wildfire season. Fire Saf J2021; 126: 103480.
45.
PatriarcaRRamosMPaltrinieriN, et al. Human reliability analysis: Exploring the intellectual structure of a research field. Reliab Eng Syst Saf2020; 203: 107102.
46.
FlanaganBEHalliseyEJAdamsE, et al. Measuring community vulnerability to natural and anthropogenic hazards: the centers for disease control and prevention’s social vulnerability index. J Environ Health2018; 80: 34–36.
YaziciMAOzbayK. Evacuation modelling in the United States: does the demand model choice matter?Transp Rev2008; 28: 757–779.
49.
NiuCNairDJZhangT, et al. Are wildfire fatalities related to road network characteristics? A preliminary analysis of global wildfire cases. Int J Disaster Risk Reduct2022; 80: 103217.
50.
LiDCovaTJDennisonPE. Setting wildfire evacuation triggers by coupling fire and traffic simulation models: a spatiotemporal GIS approach. Fire Technol2019; 55: 617–642.
51.
SogaKComfortLZhaoB, et al. Integrating traffic network analysis and communication network analysis at a regional scale to support more efficient evacuation in response to a wildfire event. University of California Berkeley Institute of Transportation Studies, Berkeley, CA. Epub ahead of print 1 May 2021. DOI: 10.7922/G21G0JJ7.
52.
SinghDPadghamLNagelK. Using MATSim as a component in dynamic agent-based micro-simulations. In: DennisLABordiniRHLespéranceY (eds) Engineering multi-agent systems. Cham: Springer International Publishing, 2020, pp.85–105.
53.
Ruiz-TagleALopez-DroguettEGrothKM. A novel probabilistic approach to counterfactual reasoning in system safety. Reliab Eng Syst Saf2022; 228: 108785.
54.
Ruiz-TagleALopez DroguettEGrothKM. Exploiting the capabilities of Bayesian networks for engineering risk assessment: causal reasoning through interventions. Risk Anal2022; 42: 1306–1324.
55.
SorichettaAHornbyGMStevensFR, et al. High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Sci Data2015; 2: 150045.
56.
MaranghidesAMellWRHawksS, et al. Camp fire reconnaissance. National Institute of Standards and Technology. Gaithersburg, MD.
57.
SetoDJonesCTrugmanAT, et al. Simulating potential impacts of fuel treatments on fire behavior and evacuation time of the 2018 camp fire in Northern California. Fire2022; 5: 37.
58.
SyifaMPanahiMLeeC-W. Mapping of post-wildfire burned area using a hybrid algorithm and satellite data: the case of the camp fire wildfire in California, USA. Remote Sens2020; 12: 623.
59.
MaranghidesALinkEMellW, et al. A case study of the camp fire – fire progression timeline (NIST TN 2135). Gaithersburg, MD: National Institute of Standards and Technology.
60.
MaranghidesAMellWHawksS, et al. preliminary data collected from the camp fire reconnaissance. National Institute of Standards and Technology. Gaithersburg, MD.
61.
ChaseJHansenP. Displacement after the camp fire: where are the most vulnerable?Soc Nat Resour2021; 34: 1566–1583.
62.
DinA. What do visualizations of administrative address data show about the camp fire in paradise, California?Cityscape2022; 24: 261–271.
RahmanRHasanSZakiMH. Towards reducing the number of crashes during hurricane evacuation: Assessing the potential safety impact of adaptive cruise control systems. Transp Res Part C Emerg Technol2021; 128: 103188.
