Collaborative Research: Household Response to Wildfire – Integrating Behavioral Science and Evacuation Modeling to Improve Community Wildfire Resilience

在过去的四十年里，由于气候变化和人口快速增长到荒地-城市界面（WUI，房屋和基础设施与荒地相遇），野火的频率，规模以及由此造成的人类和经济损失显着增加。野火风险可以显着降低三种类型的事件前家庭危险调整缓解，准备留下来，防御和生存，并准备撤离。然而，对影响采用这些危险调整的因素仍然知之甚少。现有的野火疏散模型很少考虑火灾蔓延动力学;缺乏人员，火灾危险和交通组成部分的三边整合;并且基于有限的社会行为数据。为了解决这些知识差距，该项目将整合行为数据与主动学习和目标设定技术，以增加WUI居民对事件前危险调整的采用。此外，社会行为数据将被注入交通工程模型，以创建更准确和可操作的基于代理的疏散模型（ABM）。为了实现这些目标，研究人员将与三个州的四个WUI社区合作，以（1）确定影响家庭对野火危险的事前危险调整和疏散决策的因素，以及（2）使用ABM将社会行为数据整合到野火疏散场景中，以评估替代疏散策略。区域规划人员和应急管理人员将参与测试和评估疏散协议和教育计划。该项目将扩大和加强保护行动决策模型（PADM）的能力，以解释与野火缓解，停留/防御/生存准备和疏散准备相关的复杂决策过程。具体而言，该项目的结果将推进我们对野火危害的事前风险信息的了解，并解决将多维数据集纳入野火疏散模型的迫切需要。对四个不同的WUI社区的研究将允许评估项目研究区域内外的跨人口普遍性。研究结果将产生激励家庭保护行动的最佳实践，评估基于社区信息的野火疏散建模循证策略，测试替代野火警报消息传递策略，并最终减少居民和企业的野火风险。多元化的项目团队包括早期职业科学家，学生和来自代表性不足群体的研究人员。该项目将建立在以前与社区利益相关者的合作基础上，在整个研究过程中共同产生和分享知识，确保工作将促进数据驱动的政策和资源分配。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The past four decades have seen a significant increase in wildfire frequency, magnitude, and resulting human and economic losses as driven by climate change and rapid population growth into the wildland-urban interface (WUI, where homes and infrastructure meet the wildland). Wildfire risk can be significantly reduced by three types of pre-event household hazard adjustments—mitigation, preparedness to stay, defend, and survive, and readiness to evacuate. However, the factors that influence the adoption of these hazard adjustments remain poorly understood. Existing wildfire evacuation models rarely consider fire spread dynamics; lack trilateral integration of people, fire hazard, and traffic components; and are based on limited social-behavioral data. To address these knowledge gaps, this project will integrate behavioral data with active learning and goal-setting techniques for increasing WUI residents’ adoption of pre-event hazard adjustments. In addition, social-behavioral data will be infused into transportation engineering models to create more accurate and actionable agent-based models (ABMs) for evacuation. To achieve these objectives, the researchers will collaborate with four WUI communities in three states to (1) identify factors influencing households’ pre-event hazard adjustment adoption and evacuation decision-making for wildfire hazard, and (2) integrate social-behavioral data into wildfire evacuation scenarios using ABMs to evaluate alternative evacuation strategies. Regional planners and emergency managers will be engaged to test and evaluate evacuation protocols and educational programs. This project will expand and strengthen the capability of the Protection Action Decision Model (PADM) to explain complex decision-making processes related to wildfire mitigation, stay/defend/survive preparedness, and evacuation readiness. Specifically, this project’s results will advance our knowledge in pre-event risk messaging about wildfire hazards and address the urgent need for incorporating multidimensional datasets in wildfire evacuation models. The study of four different WUI communities will allow assessment of the cross-population generalizability within and beyond the project’s study areas. The outcomes will lead to best practices for motivating households’ protective actions, assessments of community-informed evidence-based strategies for wildfire evacuation modeling, testing of alternative wildfire warning messaging strategies, and, ultimately, reduction in wildfire risk to residents and businesses. The diverse project team includes early-career scientists, students, and researchers from underrepresented groups. The project will build upon previous collaborations with community stakeholders to co-produce and share knowledge throughout the research process, ensuring that the work will promote data-driven policies and resource allocation.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.