ABSTRACT

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.

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