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
Movement Strategies Ltd, London, UK.
Department of Fire Safety Engineering, Lund University, Lund, Sweden.
Fire Technol. 2023;59(2):879-901. doi: 10.1007/s10694-023-01371-1. Epub 2023 Feb 8.
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.
The online version contains supplementary material available at 10.1007/s10694-023-01371-1.
野火的规模、频率和持续时间正在增加,并且随着环境条件的变化正在影响新的地区。本文展示了2019年在美国科罗拉多州罗克斯伯勒公园进行的一次社区疏散演练期间收集的数据集。这是一个城乡交错带社区,包括约900户家庭。通过观察和调查收集了有关社区响应几个方面的数据:初始人口位置、疏散前时间、路线使用情况以及到达疏散集合点的时间。这些数据被用作基准两个采用不同建模方法的疏散模型的输入。WUI-NITY平台和疏散管理系统模型被应用于一系列场景,其中关于疏散前延迟和所使用路线的假设根据原始数据收集方法(以及对所生成数据的解释)而有所不同。结果主要由疏散前时间输入所采用的假设驱动。在道路上车辆数量较少且交通拥堵相对有限的社区中,这是可以预期的。鉴于采用了不同的建模方法,该分析能够探索建模方法对不同数据集的敏感性。模型的性能对所使用的数据(源自观察或自我报告)以及其中涉及的疏散阶段敏感。这表明监测将数据纳入模型的影响而非仅仅监测数据本身的重要性,因为鉴于所采用的建模方法,数据以不同方式影响模型。该数据集以开放获取的方式发布,被认为对未来野火疏散建模的校准和验证工作有用。
在线版本包含可在10.1007/s10694-023-01371-1获取的补充材料。