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.

由于过去几年由野火造成的生命和财产损失，野火疏散计划已成为野生世界界面（WUI）社区的重中之重。在疏散后勤和行为方面，兼职居民和暂时人群（例如，游客）可能与全职居民有所不同，这对社区疏散计划构成了重大挑战。该工程研究启动（ERI）项目将利用大数据和计算机模型来开发一种新的野火疏散计划方法，该方法可以纳入兼职居民和瞬态人群，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，以改善当地的野火疏散计划。该项目中产生的知识将有助于撤离研究人员和从业人员更好地使用大数据以及最新的野火疏散建模技术来改善野火的公共安全。此外，该项目将支持对地理信息系统（GIS）专业人士，数据科学家/工程师以及疏散研究人员/从业人员的下一代的教育和培训。该项目将提高了解大数据的使用，并在野火疏散模型中使用野火疏散模型，以使社区与临时人群进行野火疏散计划。研究团队将进行家庭调查，以研究全日制和兼职居民在疏散后勤和行为方面的差异。然后，我们将集成火灾传播和显微镜交通模拟模型，以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后勤和行为以及其他瞬态人群。来自不同来源的各种数据将用于系统地设计一系列疏散场景，并且开发的疏散模型用于对这些疏散场景进行疏散模拟。生成的火灾周边和高分辨率车辆轨迹数据将用于得出疏散时间估计和车辆暴露数量信息。研究团队将使用GIS可视化车辆暴露数量信息。此外，将采用疏散计划方法，以为研究地点创建数据驱动的疏散计划。研究发现将通过在线研讨会，出版物，会议演讲和网络GIS应用程序进行广泛分解。该项目由人类，灾难和建筑环境（HDBE）（HDBE）共同资助，并刺激竞争性研究（EPSCOR）的既定计划（EPSCOR）。这一奖项反映了NSF的法定任务和审查的范围。

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