ERI: Advancing Understanding of Data-driven Wildfire Evacuation Planning for Communities with Transient Populations in the Wildland-Urban Interface

由于过去几年野火造成的重大生命和财产损失，野火疏散规划已成为荒地-城市界面（WUI）社区的优先事项。兼职居民和流动人口（例如，游客）在疏散物流和行为方面可能不同于全职居民，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开发一种新的野火疏散规划方法，该方法可以将兼职居民和临时人口纳入其中，并考虑不同的疏散场景。研究成果将与社区利益相关者分享，以改善当地的野火疏散计划。该项目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和最新的野火疏散建模技术，以改善野火公共安全。此外，该项目还将支持对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。该项目将促进对大数据和野火疏散模型在有流动人口的社区野火疏散规划中的使用的理解。研究小组将进行家庭调查，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，可以考虑全职和兼职居民的疏散物流和行为以及其他短暂的人口。从不同来源的各种数据将被用来系统地设计一系列的疏散方案，和开发的疏散模型是用来执行这些疏散方案的疏散模拟。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计数信息。研究小组将使用地理信息系统可视化车辆暴露计数信息。此外，将采用疏散计划方法为研究中心创建数据驱动的疏散计划。研究结果将通过在线研讨会、出版物、会议演示和Web GIS应用程序广泛传播。该项目由人类、灾害、和建筑环境（HDBE）和既定计划，以刺激竞争力的研究（EPSCoR）该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

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.