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1. Data-Driven Modeling of Evacuation Decision-Making in Extreme Weather Events

   https://doi.org/10.1007/978-3-030-93409-5_56  

   Hancock, Matthew; Halim, Nafisa; Kuhlman, Chris J; Marathe, Achla; Mozumder, Pallab; Ravi, S S; Vullikanti, Anil (January 2022, Complex Networks and their Applications)

   Data from surveys administered after Hurricane Sandy provide a wealth of information that can be used to develop models of evacuation decision-making. We use a model based on survey data for predicting whether or not a family will evacuate. The model uses 26 features for each household including its neighborhood characteristics. We augment a 1.7 million node household-level synthetic social network of Miami, Florida with public data for the requisite model features so that our population is consistent with the survey-based model. Results show that household features that drive hurricane evacuations dominate the effects of specifying large numbers of families as \early evacuators" in a contagion process, and also dominate effects of peer influence to evacuate. There is a strong network-based evacuation suppression effect from the fear of looting. We also study spatial factors affecting evacuation rates as well as policy interventions to encourage evacuation. 

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2. Predicting hurricane evacuation for local neighborhoods across a metropolitan region

   https://doi.org/10.1016/j.ijdrr.2023.104117  

   Tanim, Shakhawat H; Reader, Steven; Hu, Yujie (December 2023, International Journal of Disaster Risk Reduction)

   To mitigate the devastating impacts of hurricanes on people’s lives, communities, and societal infrastructures, disaster management would benefit considerably from a detailed understanding of evacuation, including the socio-demographics of the populations that evacuate, or remain, down to disaggregated geographic levels such as local neighborhoods. A detailed household evacuation prediction model for local neighborhoods requires both a robust household evacuation decision model and individual household data for small geographic units. This paper utilizes a recently pub- lished statistical meta-analysis for the first requirement and then conducts a rigorous population synthesis procedure for the second. Our model produces predicted non-evacuation rates for all US Census block groups for the Tampa-St. Petersburg-Clearwater Metropolitan Statistical Area for a Hurricane Irma-like storm along with their socio-demographic and hurricane impact risk profiles. Our model predictions indicate that non- evacuation rates are likely to vary considerably, even across neighboring block groups, driven by the variability in evacuation risk profiles. Our results also demonstrate how different predictors may come to the fore in influencing non-evacuation in different block groups, and that predictors which may have an outsize impact on individual household evacuation decisions, such as Race, are not necessarily associated with the greatest differentials in non-evacuation rates when we aggregate households to block group level and above. Our research is intended to provide a framework for the design of hurricane evacuation prediction tools that could be used in disaster management. 

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3. Increasing Evacuation During Disaster Events

   Kuhlman, Chris J; Marathe, Achla; Vullikanti, Anil; Halim, Nafisa; Mozumder, Pallab (May 2020, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems)

   null (Ed.)

   Timely evacuation is a standard recommendation by local agencies before disaster events such as hurricanes, which have enough advance notice. However, it has been observed in many recent disasters (e.g., Sandy), that only a small fraction of the population evacuates in time. Recent work by social scientists has examined the factors that influence household evacuation decisions; in addition to individual factors it has been found that peer effect plays a role in this decision but in two opposing ways. Specifically, households are motivated to evacuate if their neighbors evacuate. However, if too many neighbors leave then some households have concerns of looting and crime, and they choose not to evacuate. This makes the dynamics of evacuation very complex. In this paper, we use a detailed agent based model to study the dynamics of evacuation in Virginia’s coastal region. We use data from a large survey and social contagion and collective action theories to develop the model. We evaluate different strategies to increase evacuation. 

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4. Departure Timing Preference during ExtremeWeather Events: Evidence from Hurricane Evacuation Behavior

   Fan Jiang; Sisi Meng; Nafisa Halim; Pallab Mozumder (May 2022, Transportation research record)

   Hurricane evacuation has become an increasingly complicated activity in the U.S. as it involves moving many people who live along the Atlantic coast and Gulf coast within a very limited time. A good deal of research has been conducted on hurricane evacuation, but only a limited number of studies have looked into the timing aspect of evacuation. This paper intends to contribute to the literature on evacuation timing decisions by investigating what factors influence the time preference at the household level. Two hurricane survey data sets were used to analyze household evacuation behaviors across the Gulf coast as well as the Northeast and Mid-Atlantic coast in a comparative perspective. Using the Heckman selection model, we examined various factors identified in the literature on the two possible outcomes (evacuation and early evacuation). We found that the most important determinants of evacuation were prior evacuation experience, evacuation orders, and risk perceptions, while the most important determinants of early evacuation were prior evacuation experiences, days spent at the evacuation destination, and the cost of evacuation. Socioeconomic factors also influenced the two decisions but differently. These results provide implications for future hurricane evacuation planning and for improving emergency management practices. 

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5. Departure Timing Preference during Extreme Weather Events: Evidence from Hurricane Evacuation Behavior

   https://doi.org/10.1177/03611981211066901  

   Jiang, Fan; Meng, Sisi; Halim, Nafisa; Mozumder, Pallab (January 2022, Transportation Research Record: Journal of the Transportation Research Board)

   Hurricane evacuation has become an increasingly complicated activity in the U.S. as it involves moving many people who live along the Atlantic coast and Gulf coast within a very limited time. A good deal of research has been conducted on hurricane evacuation, but only a limited number of studies have looked into the timing aspect of evacuation. This paper intends to contribute to the literature on evacuation timing decisions by investigating what factors influence the time preference at the household level. Two hurricane survey data sets were used to analyze household evacuation behaviors across the Gulf coast as well as the Northeast and Mid-Atlantic coast in a comparative perspective. Using the Heckman selection model, we examined various factors identified in the literature on the two possible outcomes (evacuation and early evacuation). We found that the most important determinants of evacuation were prior evacuation experience, evacuation orders, and risk perceptions, while the most important determinants of early evacuation were prior evacuation experiences, days spent at the evacuation destination, and the cost of evacuation. Socioeconomic factors also influenced the two decisions but differently. These results provide implications for future hurricane evacuation planning and for improving emergency management practices. 

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