Modeling joint evacuation decisions in social networks: The case of Hurricane Sandy (original) (raw)
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Behavioral Model to Understand Household-Level Hurricane Evacuation Decision Making
Journal of Transportation Engineering, 2011
Hurricanes are one of the most costly natural disasters in the United States and have increased in frequency in the last few years. The critical role of evacuation, particularly for the vulnerable communities, has been realized from some disastrous evacuation experiences in recent hurricanes (for example, Hurricanes Katrina and Rita in 2005). Therefore, a thorough understanding of the determinants of evacuation behavior is needed to protect the loss of lives, especially in the vulnerable communities. However, a household's decision-making process under a hurricane risk is a very complex process influenced by many factors. This paper presents a model of household hurricane evacuation behavior accounting for households' heterogeneous behavior in decision making by using original data from Hurricane Ivan. It develops a mixed logit (also known as random-parameters logit) model of hurricane evacuation decision, where random parameters reflect the heterogeneous responses of households caused by a hurricane. We report several factors consistent with some of the previous findings, which are important for understanding household-level evacuation decision making. We also explain the varied influences of some of the determining variables on the hurricane evacuation decision.
Leveraging social networks for efficient hurricane evacuation
Transportation Research Part B: Methodological, 2015
One of the important factors affecting evacuation performance is the departure time choices made by evacuees. Simultaneous departures of evacuees can lead to overloading of road networks causing congestion. We are especially interested in cases when evacuees subject to little or no risk of exposure evacuate along with evacuees subject to higher risk of threat (also known as shadow evacuation). One of the reasons for correlated evacuee departures is higher perceived risk of threat spread through social contacts. In this work, we study an evacuation scenario consisting of a high risk region and a surrounding low risk area. We propose a probabilistic evacuee departure time model incorporating both evacuee individual characteristics and the underlying evacuee social network. We find that the performance of an evacuation process can be improved by forcing a small subset of evacuees (inhibitors) in the low risk area to delay their departure. The performance of an evacuation is measured by both average travel time of the population and total evacuation time of the high risk evacuees. We derive closed form expressions for average travel time for ER random network. A detailed experimental analysis of various inhibitor selection strategies and their effectiveness on different social network topologies and risk distribution is performed. Results indicate that significant improvement in evacuation performance can be achieved in scenarios where evacuee social networks have short average path lengths and topologically influential evacuees do not belong to the high risk regions. Additionally, communities with stronger ties improve evacuation performance.
Geophysical and Social Influences on Evacuation Decision-Making: The Case of Hurricane Irma
Atmosphere
Understanding the factors that influence evacuation decision-making among local residents is of critical importance to those involved in monitoring and managing weather-related hazards. This study examined both geophysical and social variables that we believe influenced individual decision-making on whether to stay home, seek out a public shelter, or leave the area entirely during Hurricane Irma. A 23-item survey was administered to a convenience sample of adults (n = 234) who resided within a coastal Florida county that received an evacuation warning during Hurricane Irma in 2017. Results suggested sources of information relied on through media, government, family, and social networks contributed to differences in evacuation behavior. Moreover, potential exposure to weather-related conditions, such as flooding and strong winds, along with the likelihood to use available social resources, also influenced decisions to stay or leave the threatened area. Finally, prior evacuation behav...
Joint modeling of evacuation departure and travel times in hurricanes
Hurricanes are costly natural disasters periodically faced by households in coastal and to some extent, inland areas. A detailed understanding of evacuation behavior is fundamental to the development of efficient emergency plans. Once a household decides to evacuate, a key behavioral issue is the time at which individuals depart to reach their destination. An accurate estimation of evacuation departure time is useful to predict evacuation demand over time and develop effective evacuation strategies. In addition, the time it takes for evacuees to reach their preferred destinations is important. A holistic understanding of the factors that affect travel time is useful to emergency officials in controlling road traffic and helps in preventing adverse conditions like traffic jams. Past studies suggest that departure time and travel time can be related. Hence, an important question arises whether there is an interdependence between evacuation departure time and travel time? Does departing close to the landfall increases the possibility of traveling short distances? Are people more likely to depart early when destined to longer distances? In this study, we present a model to jointly estimate departure and travel times during hurricane evacuations. Empirical results underscore the importance of accommodating an interrelationship among these dimensions of evacuation behavior. This paper also attempts to empirically investigate the influence of social ties of individuals on joint estimation of evacuation departure and travel times. Survey data from Hurricane Sandy is used for computing empirical results. Results indicate significant role of social networks in addition to other key factors on evacuation departure and travel times during hurricanes.
Estimating the Sequencing of Evacuation Destination and Accommodation Type in Hurricanes
Journal of Homeland Security and Emergency Management, 2019
Hurricanes are one of the most dangerous catastrophes faced by the USA. The associated life losses can be reduced by proper planning and estimation of evacuation demand by emergency planners. Traditional evacuation demand estimation involves a sequential process of estimating various decisions such as whether to evacuate or stay, evacuation destination, and accommodation type. The understanding of this sequence is not complete nor restricted to strict sequential ordering. For instance, it is not clear whether the evacuation destination decision is made before the accommodation type decision, or the accommodation type decision is made first or both are simultaneously made. In this paper, we develop a nested logit model to predict the relative ordering of evacuation destination and accommodation type that considers both sequential and simultaneous decision making. Household survey data from Hurricane Matthew is used for computing empirical results. Empirical results underscore the importance of developing a nested structure among various outcomes. In addition to variables related to risk perception and household characteristics, it is found that social networks also affect this decision-making process.
Modeling Evacuation Behavior Under Hurricane Conditions
Transportation Research Record: Journal of the Transportation Research Board, 2016
The understanding of evacuation behavior is critical to establishing policies, procedures, and organizational structure for an effective response to emergencies. This study specifically investigated the evacuation behavioral responses under hurricane conditions. The study aimed to explore the association between contributing factors and the evacuation decision choices as well as evacuation destination choices. Unlike previous studies that modeled each response behavior separately, this study proposed to use the structural equation modeling approach to examine the interrelationship between response behaviors. A case study was performed with the data set from a survey conducted in New Jersey. With Bayesian estimation approaches, the proposed structural equation models were estimated, and the effect of each predictive variable was captured. An important finding is that individuals’ preference to evacuate did not significantly affect their choices of evacuation destinations. In addition...
2021
Pamela Murray-Tuite, for her invaluable support, guidance, and patience towards the completion my doctoral studies. Her constructive feedback, comments, and suggestions did not only help me to grow and grasp solid understanding of hurricane evacuation research but has also helped to improve the quality of this dissertation manuscript significantly. I would also like to express my sincere gratitude to my dissertation committee members: Dr. Wayne Sarasua (Co-Chair), Dr. Jennifer H. Ogle, and Dr. Patrick Gerard for their comments and suggestions. Thank you very much! To Dr. Ruijie (Rebecca) Bian, I really appreciate your guidance, comments and suggestions during my doctoral studies at Clemson University.
A statistical analysis of the dynamics of household hurricane-evacuation decisions
Transportation, 2016
With the increasing number of hurricanes in the last decade, efficient and timely evacuation remains a significant concern. Households' decisions to evacuate/stay and selection of departure time are complex phenomena. This study identifies the different factors that influence the decision making process, and if a household decides to evacuate, what affects the timing of the execution of that decision. While developing a random parameters binary logit model of the evacuate/stay decision, several factors, such as, socioeconomic characteristics, actions by authority, and geographic location, have been
Household Evacuation Decision Making in Response to Hurricane Ike
Natural Hazards Review, 2012
This study focused on household evacuation decisions and departure timing for Hurricane Ike. The data were consistent with an abbreviated form of the Protective-Action Decision Model in which female gender, official warning messages, hurricane experience, coastal location, and environmental and social cues were hypothesized to produce perceived storm characteristics, which in turn, would produce expected personal impacts. Finally, the latter, together with perceived evacuation impediments, would determine evacuation decisions and departure timing. However, there were fewer significant predictors of perceived storm characteristics and more significant predictors of expected personal impacts and evacuation decisions than hypothesized. Also contrary to hypothesis, female gender, perceived storm characteristics, official warnings, and hurricane experience predicted departure times. However, as expected, evacuation rates declined with distance from the coast; unlike Hurricane Rita 3 years earlier, there was a very low level of shadow evacuation in inland Harris County. Finally, most households evacuated 2 days before landfall, between the time of the National Hurricane Center hurricane watch and warning, and evacuated overwhelmingly during the daytime hours.
Household-Level Model for Hurricane Evacuation Destination Type Choice Using Hurricane Ivan Data
Natural Hazards Review, 2013
Hurricanes are costly natural disasters periodically faced by households in coastal and, to some extent, inland areas. Public agencies must understand household behavior to develop evacuation plans that align with evacuee choices and behavior. This paper presents a previously unknown household-level hurricane evacuation destination type choice model. The discrete choice of destination type is modeled using a nested logit model. Although previous literature considers only houses of friends and relatives and hotels for modeling purposes, this paper incorporates public shelters, churches, and an aggregated destination type denoted other. This research found that the variables influencing this choice include hurricane position at evacuation time, household geographic location, race, income, preparation time, changes in evacuation plans, previous experiences with major hurricanes, household members working during the evacuation, and evacuation notices. The findings of this paper are useful to understand the competition among destination types and how the characteristics of the demand can be used to develop evacuation strategies, such as increasing and/or decreasing use of public shelters, and measuring the effect of evacuation notices in areas with high accessibility to hotels.