A data-driven approach to improving evacuation time estimates during wildfires for communities with part-time residents in the wildland-urban interface
A data-driven approach to improving evacuation time estimates during wildfires for communities with part-time residents in the wildland-urban interface
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DOI：
10.1016/j.ijdrr.2022.103363
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发表时间：
2022-10
影响因子：
5
通讯作者：
Dapeng Li
中科院分区：
文献类型：
--
作者：
Dapeng Li
野火对生活在荒地与城市交界处的居民构成重大威胁。野火疏散的计算机建模可以促进保护行动决策并改善野火公共安全。本研究旨在利用不同类型的数据、交通模拟模型和地理信息系统来开发数据驱动的野火疏散模型，以改进度假区的疏散时间估计。具体来说，我们在模型构建时基于多种数据考虑了家庭车辆保有量数据和第二套住房入住率。我们在案例研究中使用了加利福尼亚州特拉基的塔霍唐纳社区，并得出了一系列疏散时间估计值。结果表明，疏散时间估计值随度假区每户平均车辆数量和第二套住房入住率的变化而显着变化。该方法可以帮助事件指挥员更好地了解火灾易发社区和兼职居民在野火疏散期间的出行需求动态，并提高他们的态势感知能力。
Wildfires pose a significant threat to the residents living in the wildland-urban interface. Computerized modeling of wildfire evacuation could facilitate protective action decision-making and improve wildfire public safety. This study aims to leverage different types of data, traffic simulation model, and geographic information systems to develop a data-driven wildfire evacuation model to improve evacuation time estimates in resort areas. Specifically, we take into account household vehicle ownership data and the occupancy rate of second homes based on a variety of data in model construction. We used the Tahoe Donner neighborhood in Truckee, California in the case study and derived a series of evacuation time estimates. The results indicate that the evacuation time estimates vary significantly with the mean number of vehicles per home and second homes' occupancy rate in resort areas. The proposed method could help incident commanders better understand the dynamics of travel demand of the fire-prone communities with part-time residents during wildfire evacuation and increase their situational awareness.