Collaborative Research: Leveraging Massive Smartphone Location Data to Improve Understanding and Prediction of Behavior in Hurricanes

在这个项目中，新提供的匿名智能手机位置数据将被用来大大提高对飓风期间家庭行为的理解（例如，有多少人将撤离，何时，如何，从哪里，到哪里）。虽然以前的研究提供了有关飓风中人口行为的宝贵知识，但仍然存在重要的差距。现有的模型预测未来飓风行为的能力有限。不同类型的家庭和人（如游客或没有车辆的人）的行为差异并不为人所知。在飓风持续期间为个人展开的事件的顺序和时间也不是。这些差距在很大程度上是由于支持过去研究的传统类型的数据的局限性，调查，访谈和焦点小组。该项目将通过利用智能手机的匿名位置信息这一新型数据的可用性来促进疏散行为建模的科学，从而在理解和预测飓风疏散期间人群的行为方面取得飞跃。该项目将通过大幅提高管理未来疏散的能力，促进国家福利，造福社会。在飓风期间，官员们会做出许多重要的决定，包括发布官方疏散命令、向公众发送信息、开放避难所、运送物资和工作人员、实施特殊交通计划、为无车人口提供支持以及准备救援。所有这些都直接取决于预计有多少人撤离，何时，如何，从哪里，到哪里。通过提供飓风期间人口行为的更准确和细致入微的预测，该项目将使官员能够以更明智和有效的方式做出这些决定。为了确保调查结果能够快速有效地转化为实践，研究的设计使其能够融入应急管理人员使用的当前决策工具和流程。我们来自联邦应急管理局（FEMA）、佛罗里达和北卡罗来纳州应急管理机构的从业者合作伙伴也将帮助我们与更大的应急管理社区分享调查结果。这项研究将有助于开发一个程序，以获取和分析，在真实的时间，类似的数据，为其他疏散events. Available的新的智能手机定位数据提供了一个难得的机会，以改变人口行为的研究在飓风。这些数据提供了许多好处，包括比以前典型的样本大几个数量级;提供个人行为的连贯时间表;提供不受回忆或报告偏见影响的直接观察;在移动后24小时内可用;以及以低成本以一致的形式提供许多飓风。将新数据的力量与基于传统调查和访谈数据的领域专业知识相结合，将在五个方面推动这一领域的科学发展。首先，我们将通过使用更大，独立的数据集和过去不易测试的新假设来测试传统文献中的假设来提高知识。其次，飓风过程中可能发生多个事件，包括飓风相关事件（例如，飓风转向，加剧），官方行动（例如，发布官方命令，关闭学校）和个人事件（例如，从工作中解放出来）。每个人在飓风持续期间都会经历这些事件中的一些或全部。我们将使用序列模式挖掘来描述关键的可观察事件和动作，它们可能的序列，不同序列的概率，以及每个事件的持续时间分布。这种对个人事件的顺序和时间的建模，以前没有做过，将阐明飓风行为，官方行动，个人决策和时间标记相互作用和展开的方式，并帮助确定有希望的疏散支持干预点。第三，我们将开发新的统计模型来预测一个人在每个时间段撤离并前往特定地理目的地的概率，作为个人/家庭，官方事件，飓风，预报，时间标记和飓风形成以来过去行动的属性的函数。这些模型将通过以下方式提供更好的样本外预测能力：确定对小数据集无法观察到的行为的影响;提高预测地理目的地的能力，这对估计清除时间很重要;以及首次利用可能是最终行为领先指标的事件早期行为观察。第四，我们将测试隐含在交通模型中的路线选择假设，用于预测通关时间，并确定在疏散和再入期间道路封闭对交通模式的影响。新的数据将使测试比以前通过孤立的流量计数和调查进行的测试更加详细和全面。最后，我们将使用一般归纳方法为未来的传统研究确定新的行为和问题。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。

In this project, newly available anonymous smartphone location data will be used to dramatically improve understanding of how households behave during hurricanes (e.g., how many people will evacuate, when, how, from where, and to where). Although previous research has provided valuable knowledge about population behavior in hurricanes, important gaps remain. Available models have limited ability to predict behavior in future hurricanes. Differences in behavior across different types of households and people, such as tourists or people without vehicles, are not well known. Neither are the sequence and timing of events that unfold for individuals over the duration of a hurricane. These gaps are largely due to limitations in the traditional types of data that have supported past research—surveys, interviews, and focus groups. This project will promote the science of modeling evacuation behavior by capitalizing on the availability of a new type of data— anonymous location information from smartphones—to make a leap forward in understanding and predicting the behavior of the population during hurricane evacuations. The project will advance national welfare and benefit society by substantially improving the ability to manage future evacuations. During a hurricane, officials make many highly consequential decisions, including issuing official evacuation orders, messaging the public, opening shelters, staging materials and staff, implementing special traffic plans, executing support for vehicle-less populations, and preparing to undertake rescues. All of these depend directly on how many people are expected to evacuate, when, how, from where, and to where. By providing a more accurate and nuanced prediction of population behavior during hurricanes, this project will enable officials to make those decisions in a more informed and effective way. To ensure findings will be translated to practice quickly and effectively, the research has been designed so that it can be integrated into the current decision-making tools and processes used by emergency managers. Our practitioner partners from the Federal Emergency Management Agency (FEMA) and the Florida and North Carolina state emergency management agencies will also help us share findings with the larger emergency management community. This study will facilitate the development of a procedure to acquire and analyze, in real time, similar data for other evacuation events.Availability of new smartphone location data offers a rare opportunity to transform the study of population behavior in hurricanes. The data offers many benefits, including samples that are orders of magnitude larger than previously typical; offering cohesive timelines of individual behavior; providing direct observations not subject to recall or reporting bias; being available within 24 hours of movement; and being available at low cost in consistent form for many hurricanes. Combining the power of the new data with domain expertise based on traditional survey and interview data will advance the science in this area in five ways. First, we will improve knowledge by testing hypotheses from the traditional literature using a larger, independent dataset and new hypotheses not easily testable in the past. Second, multiple events may happen during the course of a hurricane, including hurricane-related events (e.g., hurricane turns, intensifies), official actions (e.g., issue official orders, close schools), and personal events (e.g., released from work). Each person experiences some or all of these events in a sequence over a hurricane’s duration. We will use sequential pattern mining to describe key observable events and actions, their possible sequences, the probabilities of different sequences, and duration distributions of each event. This modeling of the sequence and timing of events for individuals, which has not been done before, will illuminate the range of ways hurricane behavior, official actions, personal decisions, and time markers interact and unfold, and help identify promising points of intervention for evacuation support. Third, we will develop new statistical models to predict the probability a person will evacuate at each time period and go to a particular geographic destination as a function of attributes of the individual/household, official events, hurricane, forecast, time markers, and past actions since the hurricane formed. These models will offer improved out-of-sample predictive power by identifying influences on behavior that are not observable with small datasets; by improving the ability to predict geographic destination, which is important for estimating clearance times; and by, for the first time, taking advantage of observations of behavior early in the event that may be leading indicators of final behavior. Fourth, we will test the route choice assumptions implicit in traffic models used to predict clearance times, and determine the effects of road closures on traffic patterns during evacuation and reentry. The new data will allow testing that is more detailed and comprehensive than previously possible through isolated traffic counts and surveys. Finally, we will identify new behaviors and questions for future traditional research using a general inductive approach.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.