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

合作研究：利用海量智能手机位置数据提高对飓风行为的理解和预测

基本信息

批准号：
2002589
负责人：
Rachel Davidson
金额：
$ 34.6万
依托单位：
University of Delaware
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002589&HistoricalAwards=false
关键词：
Collaborative Research Leveraging Massive Smartphone

项目摘要

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.

在该项目中，新提供的匿名智能手机位置数据将用于极大地提高对飓风期间家庭行为的了解（例如，有多少人将撤离、何时、如何、从何处以及前往何处）。尽管人们对飓风中的人口行为的了解仍然存在很大差距，但现有模型预测未来飓风中行为的能力仍然有限。不同类型的家庭和人群（例如游客或没有车辆的人）的行为差异也不是众所周知的。事件的顺序和时间安排这些差距主要是由于支持过去研究（调查、访谈和焦点小组）的传统数据类型的局限性。该项目将通过利用来促进疏散行为建模的科学。新型数据（来自智能手机的匿名位置信息）的可用性，在理解和预测飓风疏散期间人口行为方面取得了飞跃，该项目将通过大幅提高管理能力来促进国家福利并造福社会。未来的疏散期间。飓风发生后，官员们做出了许多重大决定，包括发布官方疏散命令、向公众发送消息、开放避难所、安置物资和人员、实施特殊交通计划、为无车人群提供支持以及准备开展救援。通过对飓风期间的人口行为进行更准确、更细致的预测，该项目将使官员能够更明智、更有效地做出这些决定。以确保调查结果会。为了快速有效地转化为实践，该研究经过精心设计，可以集成到应急管理人员当前使用的决策工具和流程中，我们来自联邦紧急事务管理署 (FEMA) 以及佛罗里达州和北部地区的从业者合作伙伴。卡罗莱纳州应急管理机构还将帮助我们与更大的应急管理社区分享研究结果。这项研究将有助于开发实时获取和分析其他疏散事件的类似数据的程序。新的智能手机位置数据的可用性。改变人口行为研究的难得机会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；提供不受召回或报告偏差影响的直接观察结果；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，将以五个方式推进该领域的科学发展。首先，我们将通过测试传统文献中的假设来提高知识。使用更大、独立的数据集和新的其次，飓风过程中可能会发生多种事件，包括飓风相关事件（例如飓风转向、加强）、官方行动（例如发布官方命令、关闭学校）和个人行为。每个人在飓风持续期间都会经历一些或全部这些事件，我们将使用序列模式挖掘来描述关键的可观察事件和行动、它们可能的序列、不同序列的概率。这种对个人事件的顺序和时间分布的建模是以前从未做过的，它将阐明飓风行为、官方行动、个人决策和时间标记相互作用和展开的方式范围，并有助于提供帮助。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/家庭属性、官方活动、飓风、预报、时间标记以及此后过去的行动这些模型将通过识别小数据集无法观察到的行为影响来提供改进的样本外预测能力；通过提高预测地理目的地的能力，这对于估计清除时间非常重要；第一次，利用对事件早期行为的观察，这可能是最终行为的先行指标。第四，我们将测试用于预测通行时间的交通模型中隐含的路线选择假设，并确定道路封闭对道路封闭的影响。疏散和重返大气层期间的交通模式。新数据将允许通过孤立的流量计数和调查进行比以前更详细和更全面的测试。最后，我们将使用一般归纳方法确定未来传统研究的新行为和问题。该奖项符合 NSF 的法定使命，并被认为是值得的。通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。