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Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking

Wildfires are increasing in scale, frequency and longevity, and are affecting new locations as environmental conditions change. This paper presents a dataset collected during a community evacuation drill performed in Roxborough Park, Colorado (USA) in 2019. This is a wildland–urban interface communi...

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Autores principales: Gwynne, Steve M. V., Ronchi, Enrico, Wahlqvist, Jonathan, Cuesta, Arturo, Gonzalez Villa, Javier, Kuligowski, Erica D., Kimball, Amanda, Rein, Guillermo, Kinateder, Max, Benichou, Noureddine, Xie, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977886/
https://www.ncbi.nlm.nih.gov/pubmed/36873577
http://dx.doi.org/10.1007/s10694-023-01371-1
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author Gwynne, Steve M. V.
Ronchi, Enrico
Wahlqvist, Jonathan
Cuesta, Arturo
Gonzalez Villa, Javier
Kuligowski, Erica D.
Kimball, Amanda
Rein, Guillermo
Kinateder, Max
Benichou, Noureddine
Xie, Hui
author_facet Gwynne, Steve M. V.
Ronchi, Enrico
Wahlqvist, Jonathan
Cuesta, Arturo
Gonzalez Villa, Javier
Kuligowski, Erica D.
Kimball, Amanda
Rein, Guillermo
Kinateder, Max
Benichou, Noureddine
Xie, Hui
author_sort Gwynne, Steve M. V.
collection PubMed
description Wildfires are increasing in scale, frequency and longevity, and are affecting new locations as environmental conditions change. This paper presents a dataset collected during a community evacuation drill performed in Roxborough Park, Colorado (USA) in 2019. This is a wildland–urban interface community including approximately 900 homes. Data concerning several aspects of community response were collected through observations and surveys: initial population location, pre-evacuation times, route use, and arrival times at the evacuation assembly point. Data were used as inputs to benchmark two evacuation models that adopt different modelling approaches. The WUI-NITY platform and the Evacuation Management System model were applied across a range of scenarios where assumptions regarding pre-evacuation delays and the routes used were varied according to original data collection methods (and interpretation of the data generated). Results are mostly driven by the assumptions adopted for pre-evacuation time inputs. This is expected in communities with a low number of vehicles present on the road and relatively limited traffic congestion. The analysis enabled the sensitivity of the modelling approaches to different datasets to be explored, given the different modelling approaches adopted. The performance of the models were sensitive to the data employed (derived from either observations or self-reporting) and the evacuation phases addressed in them. This indicates the importance of monitoring the impact of including data in a model rather than simply on the data itself, as data affects models in different ways given the modelling methods employed. The dataset is released in open access and is deemed to be useful for future wildfire evacuation modelling calibration and validation efforts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10694-023-01371-1.
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spelling pubmed-99778862023-03-03 Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking Gwynne, Steve M. V. Ronchi, Enrico Wahlqvist, Jonathan Cuesta, Arturo Gonzalez Villa, Javier Kuligowski, Erica D. Kimball, Amanda Rein, Guillermo Kinateder, Max Benichou, Noureddine Xie, Hui Fire Technol Article Wildfires are increasing in scale, frequency and longevity, and are affecting new locations as environmental conditions change. This paper presents a dataset collected during a community evacuation drill performed in Roxborough Park, Colorado (USA) in 2019. This is a wildland–urban interface community including approximately 900 homes. Data concerning several aspects of community response were collected through observations and surveys: initial population location, pre-evacuation times, route use, and arrival times at the evacuation assembly point. Data were used as inputs to benchmark two evacuation models that adopt different modelling approaches. The WUI-NITY platform and the Evacuation Management System model were applied across a range of scenarios where assumptions regarding pre-evacuation delays and the routes used were varied according to original data collection methods (and interpretation of the data generated). Results are mostly driven by the assumptions adopted for pre-evacuation time inputs. This is expected in communities with a low number of vehicles present on the road and relatively limited traffic congestion. The analysis enabled the sensitivity of the modelling approaches to different datasets to be explored, given the different modelling approaches adopted. The performance of the models were sensitive to the data employed (derived from either observations or self-reporting) and the evacuation phases addressed in them. This indicates the importance of monitoring the impact of including data in a model rather than simply on the data itself, as data affects models in different ways given the modelling methods employed. The dataset is released in open access and is deemed to be useful for future wildfire evacuation modelling calibration and validation efforts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10694-023-01371-1. Springer US 2023-02-08 2023 /pmc/articles/PMC9977886/ /pubmed/36873577 http://dx.doi.org/10.1007/s10694-023-01371-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Gwynne, Steve M. V.
Ronchi, Enrico
Wahlqvist, Jonathan
Cuesta, Arturo
Gonzalez Villa, Javier
Kuligowski, Erica D.
Kimball, Amanda
Rein, Guillermo
Kinateder, Max
Benichou, Noureddine
Xie, Hui
Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking
title Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking
title_full Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking
title_fullStr Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking
title_full_unstemmed Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking
title_short Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking
title_sort roxborough park community wildfire evacuation drill: data collection and model benchmarking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977886/
https://www.ncbi.nlm.nih.gov/pubmed/36873577
http://dx.doi.org/10.1007/s10694-023-01371-1
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