ERI: Advancing understanding of data-driven wildfire evacuation planning for communities with transient populations in the wildland-urban interface

ERI：促进对荒地与城市交界处有流动人口的社区的数据驱动的野火疏散规划的理解

• 批准号: 2138647
• 负责人: Dapeng Li
• 金额: $ 20万
• 依托单位: South Dakota State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138647&HistoricalAwards=false
• 关键词: ERI; Advancing; understanding; data; driven

Due to the significant loss of life and property caused by wildfires in the past few years, wildfire evacuation planning has become a priority for communities in the wildland-urban interface (WUI). Part-time residents and transient populations (e.g., visitors) can differ from full-time residents in terms of evacuation logistics and behavior, which poses a significant challenge for community evacuation planning. This Engineering Research Initiation (ERI) project will leverage big data and computer models to develop a new wildfire evacuation planning approach that can incorporate part-time residents and transient populations and take into account different evacuation scenarios. Research outputs will be shared with community stakeholders to improve local wildfire evacuation plans. The knowledge generated in this project will help evacuation researchers and practitioners better use big data and the newest wildfire evacuation modeling techniques to improve wildfire public safety. Furthermore, this project will support education and training for the next-generation of geographic information systems (GIS) professionals, data scientists/engineers, and evacuation researchers/practitioners.This project will advance understanding the use of big data and coupled wildfire evacuation models in wildfire evacuation planning for communities with transient populations. The research team will conduct a household survey to study the difference between full-time and part-time residents with regard to evacuation logistics and behavior. Then we will integrate fire spread and microscopic traffic simulation models to develop a coupled wildfire evacuation model that can incorporate full-time and part-time residents’ evacuation logistics and behavior and other transient populations. A variety of data from different sources will be used to systematically design a series of evacuation scenarios, and the developed evacuation model is used to perform evacuation simulations for these evacuation scenarios. The generated fire perimeter and high-resolution vehicle trajectory data will be used to derive evacuation time estimates and vehicle exposure count information. The research team will use GIS to visualize vehicle exposure count information. Additionally, the evacuation planning approach will be employed to create data-driven evacuation plans for the study site. The research findings will be broadly disseminated via an online workshop, publications, conference presentations, and a Web GIS application.This project is jointly funded by Humans, Disasters, and the Built Environment (HDBE) and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于过去几年山火造成了巨大的生命和财产损失，山火疏散计划已成为荒地与城市交界处（WUI）社区的首要任务。疏散物流和行为方面与全职居民不同，这对社区疏散规划提出了重大挑战。该工程研究启动（ERI）项目将利用大数据和计算机模型来制定新的野火疏散规划。该方法可以纳入兼职居民和流动人口，并考虑到不同的疏散场景，研究成果将与社区利益相关者共享，以改进当地的野火疏散计划，该项目中产生的知识将帮助疏散研究人员和从业人员更好地使用大数据。此外，该项目还将支持下一代地理信息系统（GIS）专业人员、数据科学家/工程师和疏散研究人员/从业人员的教育和培训。进一步了解大数据和耦合山火疏散模型在流动人口社区山火疏散规划中的应用 研究团队将进行入户调查，以研究全职和兼职居民在疏散物流和行为方面的差异。然后，我们将整合火灾蔓延和微观交通模拟模型，开发一个耦合的野火疏散模型，该模型可以结合全职和兼职居民的疏散物流和行为以及其他流动人口的不同来源的数据。系统地设计一个研究团队将使用所开发的疏散模型对这些疏散场景进行疏散模拟，并使用生成的火灾边界和高分辨率车辆轨迹数据来得出疏散时间估计和车辆暴露计数信息。此外，还将采用疏散规划方法为研究地点制定数据驱动的疏散计划，研究结果将通过在线研讨会、出版物、会议演示等方式广泛传播。该项目由人类、灾害和建筑环境 (HDBE) 以及刺激竞争性研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并通过评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

A data-driven approach to improving evacuation time estimates during wildfires for communities with part-time residents in the wildland-urban interface

• DOI: 10.1016/j.ijdrr.2022.103363
• 发表时间: 2022-10
• 期刊: International Journal of Disaster Risk Reduction
• 影响因子: 5
• 作者: Dapeng Li

Using Structure Location Data to Map the Wildland–Urban Interface in Montana, USA

使用结构位置数据绘制美国蒙大拿州荒地和城市界面的地图

• DOI: 10.3390/fire5050129
• 发表时间: 2022
• 期刊: Fire
• 作者: Ketchpaw, Alexander R.;Li, Dapeng;Khan, Shahid Nawaz;Jiang, Yuhan;Li, Yingru;Zhang, Ling
• 通讯作者: Zhang, Ling