The simulation of wildland-urban interface fire evacuation: The WUI-NITY platform

Wahlqvist, Jonathan; Ronchi, Enrico; Gwynne, Steven; Kinateder, Max; Rein, Guillermo; Mitchell, Harry; Bénichou, Noureddine; Ma, Chunyun; Kimball, Amanda; Kuligowski, Erica D.

doi:10.1016/j.ssci.2020.105145

Cited by 40 publications

(23 citation statements)

References 44 publications

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“…Figure 1 shows an example diagram of timescales for fire spread and egress in a bushfire-prone area, adapted from Figure 7 in Li, Cova and Dennison (2019) and previously published in Wahlqvist et al (2021). In this figure, the dotted line describes the trigger perimeter designated around the affected community and the orange perimeters (solid lines) indicate the fire front at specific time increments approaching the community.…”

Section: Additional Features Of the Wui-nity Platformmentioning

confidence: 99%

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October 2022

2022

AJEM

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The Australian Journal of Emergency Management is Australia's premier journal in emergency management. Its format and content are developed with reference to peak emergency management organisations and the emergency management sectors-nationally and internationally. The journal focuses on both the academic and practitioner reader. Its aim is to strengthen capabilities in the sector by documenting, growing and disseminating an emergency management body of knowledge. The journal strongly supports the role of the Australian Institute for Disaster Resilience as a national centre of excellence for knowledge and skills development in the emergency management sector. Papers are published in all areas of emergency management. The journal encourages empirical reports but may include specialised theoretical, methodological, case study and review papers and opinion pieces.

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Section: Additional Features Of the Wui-nity Platformmentioning

confidence: 99%

“…While the output is provided in 2D, the model has the potential to integrate a first-person 3D user-experience that could be used for visualisation purposes as well as future virtual reality interaction with the evacuation scenario space (Wahlqvist et al 2021).…”

Section: Wui-nity Model Overviewmentioning

confidence: 99%

October 2022

2022

AJEM

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“…Compared with survey methods, a GPS dataset is collected automatically by a mobile device, has a large sample size (e.g., millions of observations), and provides approximate timestamps and GPS locations of people’s movements. These characteristics make GPS data an appropriate data source for capturing people’s routing behavior during the evacuation, which can be used to validate existing evacuation simulation models 11 , 12 . However, there is no publicly available GPS dataset to allow researchers and practitioners to analyze people’s routing behavior during wildfire evacuations .…”

Section: Background and Summarymentioning

confidence: 99%

“…This dataset can be used to study how households responded to wildfire evacuation warnings and orders and to explore the impacts of evacuation notifications on traffic flows. The potential use of this dataset includes: (1) analyzing and modeling evacuee route choice behavior; (2) estimating traffic conditions (e.g., travel time, traffic flow and speed); and (3) validating existing wildfire evacuation simulation models 11 , 12 .…”

Section: Background and Summarymentioning

confidence: 99%

A highway vehicle routing dataset during the 2019 Kincade Fire evacuation

Zhao

Lovreglio

et al. 2022

Sci Data

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As the threat of wildfire increases, it is imperative to enhance the understanding of household evacuation behavior and movements. Mobile GPS data provide a unique opportunity for studying evacuation routing behavior with high ecological validity, but there are little publicly available data. We generated a highway vehicle routing dataset derived from GPS trajectories generated by mobile devices (e.g., smartphones) in Sonoma County, California during the 2019 Kincade Fire that started on October 23, 2019. This dataset contains 21,160 highway vehicle routing records within Sonoma County from October 16, 2019 to November 13, 2019. The quality of the dataset is validated by checking trajectories and average travel speeds. The potential use of this dataset lies in analyzing and modeling evacuee route choice behavior, estimating traffic conditions during the evacuation, and validating wildfire evacuation simulation models.

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“…It too uses wildfire simulations to calculate arrival times, and then creates a weighted point network to find the trigger boundary. The long-term goal of PERIL is to be integrated in the greater WUINITY project [Mallick, 2021] [Ronchi et al 2020] [Wahlqvist et al 2021. WUINITY is a project aiming to provide a coupled wildfire-evacuation model that accounts for wildfire propagation, pedestrian and vehicular evacuation.…”

Section: Peril and K-perilmentioning

confidence: 99%

k-PERIL: probabilistic creation of trigger boundaries for rural communities evacuating from a wildfire.

Kalogeropoulos¹,

Mitchell²,

Rein³

2022

Advances in Forest Fire Research 2022

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Evacuation is a critical part of wildfire emergencies. Emergency managers must be prepared to issue evacuation notices to areas that could be in the path of a wildfire to protect lives. There are methods of establishing when an evacuation should be called for any given urban area. One option is the creation of trigger boundaries around populated areas. This boundary is formed so that, should the wildfire cross it, a triggered evacuation will be complete before the wildfire becomes a threat to the area by threatening the community. Models already exist for calculating the trigger boundary around an area, and each have their strengths and limitations. WUIVAC by Li et. al. (2015) is a robust tool that can calculate the trigger boundary of an urban area, for any one simulated fire. However, it can only model trigger boundaries for one wildfire at a time and thus cannot capture the susceptibility of the area to any possible wildfire. Ramirez et. al. (2019) created a tool to calculate the probabilistic trigger boundary of an area by simulating all possible fires that may threaten the urban area. This tool is effective for management and planning, but is incredibly resource-intensive and limited in terms of letting the user specify which area is most likely to suffer from a wildfire. To address this, the k-PERIL algorithm was developed to calculate the trigger boundary of an urban area, as an upgraded version of the PERIL algorithm (Mitchell et. al, 2019). The user can load a single simulated wildfire, through software like FARSITE, and an evacuation time and get an exact trigger boundary for a specified urban area. The user can also load multiple wildfire simulation results for a given area, and K-PERIL will then generate a probabilistic trigger area. The user can then choose to retrieve a singular boundary of the area based on a probability value; It was made as part of the WUINITY project, by Ronchi et. al (2020), a self-contained wildfire simulation and evacuation planning tool. To introduce k-PERIL as a tool of calculating trigger boundaries, this paper presents the a study on creating trigger boundaries for Roxborough Park, an urban area near Denver, CO, USA. A fire ignition area was specified and a number of wildfires with varying weather inputs were created, and a probabilistic boundary was created. The testing program allowed the user to specify the average weather values and standard deviations of their area of interest, specify an area where ignition is most likely, and specify the number of simulated wildfires. Testing for single-wildfire boundaries will be conducted in the full paper. The results show that the results follow the current understanding of wildfire propagation. The resulting shapes are more accurate when more test cases are loaded, and are wider when the input variables are more varied. K-PERIL can thusly be used to create both specific and probabilistic boundaries around an inhabited area, either for immediate response or long-term planning.

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The simulation of wildland-urban interface fire evacuation: The WUI-NITY platform

Cited by 40 publications

References 44 publications

October 2022

October 2022

A highway vehicle routing dataset during the 2019 Kincade Fire evacuation

k-PERIL: probabilistic creation of trigger boundaries for rural communities evacuating from a wildfire.

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