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  1. 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 asmore »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.« less
    Free, publicly-accessible full text available May 1, 2023
  2. We study evacuation dynamics in a major urban region (Miami, FL) using a combination of a realistic population and social contact network, and an agent-based model of evacuation behavior that takes into account peer influence and concerns of looting. These factors have been shown to be important in prior work, and have been modeled as a threshold-based network dynamical systems model (2mode-threshold), which involves two threshold parameters|for a family's decision to evacuate and to remain in place for looting and crime concerns|based on the fraction of neighbors who have evacuated. The dynamics of such models are not well understood, andmore »we observe that the threshold parameters have a significant impact on the evacuation dynamics. We also observe counter-intuitive effects of increasing the evacuation threshold on the evacuated fraction in some regimes of the model parameter space, which suggests that the details of realistic networks matter in designing policies.« less
    Free, publicly-accessible full text available January 1, 2023
  3. 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 familiesmore »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.« less
    Free, publicly-accessible full text available January 1, 2023
  4. Free, publicly-accessible full text available October 1, 2022
  5. We analyzed data from a survey administered to 1,212 respondents living in superstorm Hurricane Sandy-affected areas. We estimated the effect of having experienced hurricane-induced disruptions to utility services, such as electricity, water, gas, phone service, and public transportation, on having an evacuation plan. Around 39% of respondents reported having an evacuation plan in case a hurricane affects their neighborhood this year. Respondents who had experienced disruptions to electricity supply had an approximately 11 percentage-point higher likelihood of having an evacuation plan than those who had experienced no such disruptions. Respondents who had experienced monetary losses from Hurricane Sandy had aroundmore »a five percentage-point higher likelihood of having an evacuation plan compared with those who had not. Among control variables, prior evacuation, distance to the coastline, residence in a flood zone, concern about the impacts of future natural disaster events, had window protection, and household members being disabled, each had an association with residents’ future evacuation planning and hurricane preparedness. In light of these findings, we discuss the policy implications of our findings for improving disaster management in hurricane-prone areas.« less
    Free, publicly-accessible full text available October 1, 2022
  6. Neighborhood e ects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model, 2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of 2mode-threshold in di fferent networks, and fimore »nd signi ficant diff erences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the e ffects of modeling parameters on evacuation decision dynamics. Speci fically, we quantify the effects of (i) network generation parameters, (ii) stochasticity in the social network generation process, (iii) model types (2mode-threshold vs. stan- dard threshold models), (iv) 2mode-threshold model parameters, (v) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting e ect can overpredict evacuation rates by as much as 50%.« less
  7. We study evacuation dynamics in a major urban region (Mi- ami, FL) using a combination of a realistic population and social contact network, and an agent-based model of evacuation behavior that takes into account peer influence and concerns of looting. These factors have been shown to be important in prior work, and have been modeled as a threshold-based network dynamical systems model (2mode-threshold), which involves two threshold parameters - for a family's decision to evacuate and to remain in place for looting and crime concerns - based on the fraction of neighbors who have evacuated. The dynamics of such modelsmore »are not well understood, and we observe that the threshold parameters have a signifi cant impact on the evacuation dynamics. We also observe counter-intuitive eff ects of increasing the evacuation threshold on the evacuated fraction in some regimes of the model parameter space, which suggests that the details of realistic networks matter in designing policies.« less
  8. 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 e ects of specifying large numbers ofmore »families as "early evacuators" in a contagion process, and also dominate e ffects of peer influence to evacuate. There is a strong network-based evacuation suppression eff ect from the fear of looting. We also study spatial factors a ecting evacuation rates as well as policy interventions to encourage evacuation.« less
  9. 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 concernsmore »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.« less
  10. Recent results from social science have indicated that neighborhood effects have an important role in an evacuation decision by a family. Neighbors evacuating can motivate a family to evacuate. On the other hand, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold models. Here, we propose a new graph dynamical system model, 2mode-threshold, which captures such behaviors. We study the dynamical properties of 2mode-threshold in different networks, and find significant differences frommore »a standard threshold model. We demonstrate the utility of our model through agent based simulations on small world networks of Virginia Beach, VA. We use it to understand evacuation rates in this region, and to evaluate the effects of the model and of different initial conditions on evacuation decision dynamics.« less