Hazards SEES: Bridging Information, Uncertainty, and Decision-Making in Hurricanes using an Interdisciplinary Perspective

为了更有效地应对飓风，应急管理人员需要更好的决策支持工具，以考虑到人口对飓风预报不确定性的反应，以及家庭决策对关键基础设施系统的影响。 例如，飓风桑迪估计造成了700多亿美元的损失，飓风卡特里娜造成了重大生命损失;在这两种情况下，由于关键交通系统的混乱，情况变得更糟。我们正在通过开发下一代数据驱动工具来满足这一需求，这些工具用于捕获和减轻飓风等灾害的不确定性。 使用来自各种来源的数据，我们正在开发新的理解家庭层面的行为，个人和机构如何处理不确定性在不同的情况下的飓风发作，以及家庭的决定对全市交通拥堵的后果。我们正在开发数据驱动的建模，社会科学和计算系统科学方法，利用数据收集的最新进展，以提高疏散的有效性和拯救生命。我们正在通过飓风后的邮件调查，个人访谈，网络实验，社交媒体和过程跟踪软件收集新的数据，并开发新的综合科学方法来模拟家庭和其他利益相关者的家庭行为和社会网络效应。使用这些数据和方法，我们正在模拟飓风的疏散物流，使用计算科学作为支持学科。因此，我们提供了一个整体的方法来表征，测量和分析飓风疏散建模，社交网络，家庭决策和随机交通建模的各个方面的不确定性。 该项目的知识进步将影响多个学科，包括应急管理，复杂系统科学，运输工程和计算科学。 我们的研究成果将有助于应急管理人员和机构预测交通和避难需求，并通过将家庭行为信息与交通模拟相结合来改善飓风前的社区规划。这些改进将导致更安全和更有效的疏散，降低成本，减少压力，最重要的是，减少生命损失。

In order to respond more effectively to hurricanes, emergency managers need better decision support tools that account for the response of populations to uncertainty in hurricane forecasts, as well as the consequences of household decisions on key infrastructure systems. For example, Hurricane Sandy is estimated to have caused more than $70 billion in losses, and Hurricane Katrina caused significant loss of life; in both cases, the situation was made worse due to chaos in key transportation systems. We are addressing this need by developing next generation data-driven tools for capturing and mitigating uncertainty in hazards such as hurricanes. Using data from various sources, we are developing new understanding of household level behaviors, how individuals and agencies process uncertainty at different instances of the hurricane onset, and the consequences of the household decisions on citywide traffic congestion. We are developing data-driven modeling, social science, and computational systems science approaches leveraging recent advancements in data gathering in order to improve the effectiveness of evacuations and save lives. We are collecting novel data through post-hurricane mail surveys, personal interviews, web experiments, social media, and process tracing software and developing new integrative scientific approaches to modeling household level behaviors and social network effects across households and other stakeholders. Using these data and methods, we are modeling evacuation logistics for hurricanes, using computational sciences as a supporting discipline. We are thus providing a holistic approach to characterize, measure, and analyze uncertainty in various aspects of hurricane evacuation modeling, social networks, household decision-making, and stochastic traffic modeling. The advances in knowledge from this project will impact multiple disciplines including emergency management, complex systems science, transportation engineering, and computational sciences. Our research outcomes will assist emergency managers and agencies to anticipate transportation and sheltering needs and to improve community planning prior to hurricanes by integrating household behavior information with traffic simulation. These improvements will lead to safer and more effective evacuations at lower cost, reduced stress, and most importantly, with less loss of life.