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Protective actions for hurricane threats are a function of the environmental and information context; individual and household characteristics, including cultural worldviews, past hurricane experiences, and risk perceptions; and motivations and barriers to actions. Using survey data from the Miami-Dade and Houston-Galveston areas, we regress individuals' stated evacuation intentions on these factors in two information conditions: (1) seeing a forecast that a hurricane will hit one's area, and (2) receiving an evacuation order. In both information conditions having an evacuation plan, wanting to keep one's family safe, and viewing one's home as vulnerable to wind damage predict increased evacuation intentions. Some predictors of evacuation intentions differ between locations; for example, Florida respondents with more egalitarian worldviews are more likely to evacuate under both information conditions, and Florida respondents with more individualist worldviews are less likely to evacuate under an evacuation order, but worldview was not significantly associated with evacuation intention for Texas respondents. Differences by information condition also emerge, including: (1) evacuation intentions decrease with age in the evacuation order condition but increase with age in the saw forecast condition, and (2) evacuation intention in the evacuation order condition increases among those who rely on public sources of information on hurricane threats, whereas in the saw forecast condition evacuation intention increases among those who rely on personal sources. Results reinforce the value of focusing hurricane information efforts on evacuation plans and residential vulnerability and suggest avenues for future research on how hurricane contexts shape decision making.
Research on evacuation from natural disasters has been published across the peer-reviewed literature among several disparate disciplinary outlets and has suggested a wide variety of predictors of evacuation behavior. We conducted a systematic review to summarize and evaluate the current literature on demographic, storm-related, and psychosocial correlates of natural disaster evacuation behavior. Eighty-three eligible papers utilizing 83 independent samples were identified. Risk perception was a consistent positive predictor of evacuation, as were several demographic indicators, prior evacuation behavior, and having an evacuation plan. The influence of prior experiences, self-efficacy, personality, and links between expected and actual behavior were examined less frequently. Prospective, longitudinal designs are relatively uncommon. Although difficult to conduct in postdisaster settings, more prospective, methodologically rigorous studies would bolster inferences. Results synthesize the current body of literature on evacuation behavior and can help inform the design of more effective predisaster evacuation warnings and procedures.
This study collected data on the evacuation from Hurricane Lili to answer questions about households’ reliance on information sources, the factors affecting their decisions to evacuate, the timing of their hurricane evacuation decisions, and the time it took them to prepare to evacuate. The results replicated previous findings on the sources of hazard information, evacuation concerns, and the timing of evacuation decisions. In addition, they provide new information about evacuation preparation times and the finding that household characteristics are uncorrelated with evacuation decision times or evacuation preparation times.
Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers’ decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.
Researchers have examined a wide range of factors that affect evacuation decisions after people hear hurricane forecasts and other information. This review of the literature focuses on three broad areas of research that often overlap: warning, risk perception, and evacuation research. Whereas it is challenging to demarcate the literature along these lines, we believe each of these areas represents important dimensions of evacuation decision making. The literature on warning focuses to varying degrees on warning as a social process, rather than a simple result of hearing official warnings. Warnings by themselves do not motivate evacuation—people must perceive risk. The extensive literature on objective and subjective processes in risk perception has to be evaluated. The review concludes with a focus on some important work in modeling evacuation and evacuation decision-making. Finally, we present recommendations for future research that draws on the strength of earlier work while focusing more directly on risk, the information included in hurricane forecasts, and the timing of those forecasts.
This article examines the evacuation behavior of residents in two South Carolina communities, Hilton Head and Myrtle Beach, during the 1996 hurricane season. Two hurricanes that approached South Carolina but hit in North Carolina allowed us to study the impact of repeated “false alarms”; (evacuations ordered based on expectations of a hurricane landfall that proved to be wrong). Differences in evacuation behavior, specific information and concerns prompting evacuation, and the reliability of information sources between hurricane events are examined to determine the impact of false alarms on the credibility of warning systems. Data were derived from a face‐to‐face survey of residents 2 weeks after Hurricane Fran in September 1996. We found that the role of official advisories was more limited than reported in previous research as people sought information on more diverse sets of concerns in their decision making. Reliance on the media and the Weather Channel, in particular, for storm characteristics and advisories was an important factor in evacuation decision making during both hurricane events. The perceived lack of reliability of gubernatorial warnings coupled with dependence on the media suggests that residents find other sources of information more personally relevant. Thus, while residents do not find that officials are “crying wolf,”; they are searching elsewhere for information to assess their own risk—what does it mean to me if there is a wolf? This increased attention toward individual differences in perceived threat may become more pronounced in future evacuations from hurricanes.
Developing an effective evacuation strategy for hurricane zones presents challenges to emergency planners because of spatial differences in geophysical risk and social vulnerability. This study examines spatial variability in evacuation assistance needs as related to the hurricane hazard. Two quantitative indicators are developed: a geophysical risk index, based on National Hurricane Center and National Flood Insurance Program data, and a social vulnerability index, based on census information. These indices are combined to determine spatial patterns of evacuation assistance needs in Hillsborough County, Florida. Four evacuation dimensions are analyzed: population traits and building structures, differential access to resources, special evacuation needs, and a combination of variables. Results indicate that geophysical risk and social vulnerability can produce different spatial patterns that complicate emergency management. Different measures of social vulnerability also confound evacuation strategies and can result in ineffective practices. It is argued that careful consideration be given to the characteristics of local populations.
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.
How can a large building with many occupants be evacuated in minimum time, and where are bottlenecks likely to occur in such an evacuation? In order to address this question we have constructed a family of three network building evacuation models. The most general model of interest, called the dynamic model, represents the evacuation of a building as it evolves over time, where time is represented discretely by consecutive time periods. The dynamic model triply optimizes in the sense that while directly minimizing the average over the occupants of the number of periods each needs to exit the building, it simultaneously maximizes the total number of people evacuating the building during periods 1 through p for all values of p, and also minimizes the time period in which the last evacuee exits the building. Coincident with the triple optimization, each arc dual variable indicates whether or not the building component the arc represents is a bottleneck in any period during the evacuation. The other two models in the family, referred to as the graphical model and the intermediate model, are smaller in scope than the dynamic model (they treat time as a parameter and are not time-dependent) but are easier to use, and offer alternative approaches which can provide some of the same insights as the dynamic model. We model the evacuation of an actual eleven floor building with 323 people, four elevators, and two stairwells, and compare model results with results of an observed building evacuation. We believe our models provide useful new tools for the analysis of building evacuability, and have the potential to facilitate the study of the interrelationships with building design, building redesign, and building evacuability.
The 2011 Great East Japan Earthquake/Tsunami was a magnitude 9.0 Mw event that destroyed most structural tsunami countermeasures. However, approximately 90% of the estimated population at risk from the tsunami survived due to rapid evacuation to higher ground or inland. In this paper, we introduce an evacuation model integrated with a numerical simulation of a tsunami and a casualty estimation evaluation. The model was developed in Netlogo, a multi-agent programming language and modeling environment for simulating complex phenomena. GIS data are used as spatial input information for road and shelter locations. Tsunami departure curves are used as the start time for agents deciding to evacuate in the model. Pedestrians and car drivers decide their own goals and search for a suitable route through algorithms that are also used in the video game and artificial intelligence fields. Bottleneck identification, shelter demand, and casualty estimation are some of the applications of the simulator. A case study of the model is presented for the village of Arahama in the Sendai plain area of Miyagi Prefecture in Japan. A stochastic simulation with 1,000 repetitions of evacuation resulted in a mean of 82.1% (SD=3.0%) of the population evacuated, including a total average of 498 agents evacuating to a multi-story shelter. The results agree with the reported outcome of 90% evacuation and 520 sheltered evacuees in the event. The proposed model shows the capability of exploring individual parameters and outcomes. The model allows observation of the behavior of individuals in the complex process of tsunami evacuation. This tool is important for the future evaluation of evacuation feasibility and shelter demand analysis.
SUMMARY Brown trout of similar length and weight were fed a standard meal which contained a known number of food organisms of the same size‐group and taxon (seven taxa were used). The weight of digestible organic matter in a trout stomach decreased exponentially with time. i.e. at a constant relative rate. At a particular water temperature, the food organisms were either evacuated from the stomach at similar rates (Group 1: Gammarus pulex, Baetis rhodani, Chironomidae, Oligochaetes) or at progressively slower rates (Group 2: Protonemura meyeri, Hydropsyche spp., Tenebrio molitor). Rates of gastric evacuation were not significantly different for food organisms of different size groups of the same taxon, or for different sized meals, or for different sizes of trout (range 20–30 cm), or for mixed and multiple meals (three meals over 16 h). Times are given for the gastric evacuation of 50%, 75%, 90% and 99% of the digestible organic matter in a meal. Starvation periods of 1, 2, 3,4 and 5 days prior to feeding did not affect evacuation rates. The rates were slightly, but not significantly, slower for starvation periods of 6 and 7 days, and were often significantly slower for starvation periods of 10, 15 and 20 days. Evacuation rates increased exponentially with increasing water temperature. It was possible to estimate both the rate and time for the gastric evacuation of different meals at water temperatures between 3–8°C and 19·1°C.
This study investigates the effectiveness of simultaneous and staged evacuation strategies using agent-based simulation. In the simultaneous strategy, all residents are informed to evacuate simultaneously, whereas in the staged evacuation strategy, residents in different zones are organized to evacuate in an order based on different sequences of the zones within the affected area. This study uses an agent-based technique to model traffic flows at the level of individual vehicles and investigates the collective behaviours of evacuating vehicles. We conducted simulations using a microscopic simulation system called Paramics on three types of road network structures under different population densities. The three types of road network structures include a grid road structure, a ring road structure, and a real road structure from the City of San Marcos, Texas. Default rules in Paramics were used for trip generation, destination choice, and route choice. Simulation results indicate that (1) there is no evacuation strategy that can be considered as the best strategy across different road network structures, and the performance of the strategies depends on both road network structure and population density; (2) if the population density in the affected area is high and the underlying road network structure is a grid structure, then a staged evacuation strategy that alternates non-adjacent zones in the affected area is effective in reducing the overall evacuation time.
A survey of coastal South Carolina residents addressed the role of household decisions in amplifying demand on transportation infrastructure during 1999’s Hurricane Floyd evacuation. The evacuation rate averaged 65% (±4.2%) in coastal evacuation areas. Three major findings reveal that traffic problems are becoming a major consideration in whether people evacuate. How they evacuate is emerging as an issue for evacuation traffic planning. First, about 25% of households took two or more cars. Nearly 50% of evacuees left in one 6-h period. Major traffic pressure developed on the Interstate system, particularly Interstate-26. Second, while the majority of respondents carried road maps, only 51% of that group used them to determine their route. Many decided to stay on the Interstate despite the congestion. Finally, the majority of South Carolinian residents traveled distances greater than necessary for safe sheltering and more than in past hurricanes. Transportation issues will become more important in coastal evacuations as traffic problems impinge on peoples’ ability to get out of harm’s way and ultimately influence their decisions to evacuate.
Residential development in fire-prone wildlands is occurring at an unprecedented rate. Community-based evacuation planning in many areas is an emerging need. In this paper we present a method for using microscopic traffic simulation to develop and test neighborhood evacuation plans in the urban–wildland interface. The method allows an analyst to map the subneighborhood variation in household evacuation travel times under various scenarios. A custom scenario generator manages household trip generation, departure timing, and destination choice. Traffic simulation, route choice, and dynamic visualization are handled by a commercial system. We present a case study for a controversial fire-prone canyon community east of Salt Lake City, Utah. GIS was used to map the spatial effects of a proposed second access road on household evacuation times. Our results indicate that the second road will reduce some household travel times much more than others, but all evacuation travel times will become more consistent.
This statistical meta-analysis (SMA) examined 38 studies involving actual responses to hurricane warnings and 11 studies involving expected responses to hypothetical hurricane scenarios conducted since 1991. The results indicate official warnings, mobile home residence, risk area residence, observations of environmental (storm conditions) and social (other people’s behavior) cues, and expectations of severe personal impacts, all have consistently significant effects on household evacuation. Other variables—especially demographic variables—have weaker effects on evacuation, perhaps via indirect effects. Finally, the SMA also indicates that the effect sizes from actual hurricane evacuation studies are similar to those from studies of hypothetical hurricane scenarios for 10 of 17 variables that were examined. These results can be used to guide the design of hurricane evacuation transportation analyses and emergency managers’ warning programs. They also suggest that laboratory and Internet experiments could be used to examine people’s cognitive processing of different types of hurricane warning messages.
We study the evacuation process from a classroom by means of experiments and simulations. The evacuation of students from a classroom is observed by video cameras, and the escape time of each student is measured. Our experimental results are compared with simulations based on a lattice gas model of pedestrian flows. We find that the empirically identified inefficiencies of the evacuation process can be well reproduced. Our particular focus is on the spatial dependence of the escape times on the initial positions, which is highly significant. The escape time distribution turns out to be rather broad due to a jamming (queuing) of the students at the exit, which determines not only the saturation flow (capacity) but also the temporal characteristics of the evacuation dynamics.
Studies of hurricane evacuation have often noted that women are more likely than men to evacuate, yet few examined those differences and tried to explain them. This paper undertakes a series of bivariate and multivariate analyses to examine the relationship between evacuation and gendered variations in socioeconomic status, care-giving roles in the household, evacuation incentives, exposure to risks, and perception of risk. A series of hypotheses are developed and tested in order to help explain why women are more likely than men to evacuate. The data used come from a cross-sectional survey of 1,050 coastal North Carolina households affected by Hurricane Bonnie, which made landfall near Wilmington, N. C., on August 25, 1998. Results from a series of bivariate and multivariate logistic regression analyses indicate that women are more likely to evacuate than men because of socially constructed gender differences in care-giving roles, access to evacuation incentives, exposure to risk, and perceived risk. We find, in part, that women are more likely to evacuate because, compared to men, they live at greater exposure to risk and have a heightened perception of risk. Yet, those men who are at greater risk and do perceive heightened risk are more likely to evacuate than women with comparable risk exposure and perception. Future studies of disaster response should distinguish clearly between the intention to evacuate and the capacity to do so.
Researchers have conducted sample surveys following at least twelve hurricanes from 1961 through 1989 in almost every state from Texas through Massachutts. The resulting database is larger than that for any other hazard, and many generalizations are feasible concerning factors accounting for variation in response to hurricane threats. Risk area and actions by public officials are the most important variables affecting public response. When public officials are aggressive in issuing evacuation notices and disseminate the messages effectively. over 90 percent of the residents of high-risk barrier islands and open coasts evacuate. People hearing, or believing they hear, official evacuation advisories or orders are more than twice as likely to leave in most locations. A greater percentage of mobile home dwellers evacuate than occupants of other housing, especially in modelate-risk and low-risk areas. General knowledge about hurricanes and hurricane safety is weakly related or unrelated to evacuation, but belief that one's own home is subject to flooding is strongly associated with whether the occupant leaves. Length of residence in hurricane prone areas and hurricane experience are not good predictors of response. The great majority of people who evacuate unnecessarily in one hurricane will still leave in future threats.
Abstract: The optimal shelter locations for the flood evacuation planning are studied in this paper. We assume that the authority can control the traffic in certain part of the network while the evacuees choose which shelter to go and by which route. The shelter location problem is posed as a Stackelberg game, consisting of the leader (authority) determining the shelter locations to minimize the total evacuation time and the follower (evacuees) choosing the destination (shelter) and route to evacuate. The problem is formulated as a bi-level programming. The upper level problem is a location problem that models the authority’s decision. A combined distribution and assignment (CDA) model is proposed to model the evacuees ’ decision as the lower level problem. In this study, the bi-level programming problem is solved using genetic algorithm. Numerical example with a real world network is given to demonstrate the application of the proposed model.
IMPORTANCE: Prehospital blood product transfusion in trauma care remains controversial due to poor-quality evidence and cost. Sequential expansion of blood transfusion capability after 2012 to deployed military medical evacuation (MEDEVAC) units enabled a concurrent cohort study to focus on the timing as well as the location of the initial transfusion. OBJECTIVE: To examine the association of prehospital transfusion and time to initial transfusion with injury survival. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of US military combat casualties in Afghanistan between April 1, 2012, and August 7, 2015. Eligible patients were rescued alive by MEDEVAC from point of injury with either (1) a traumatic limb amputation at or above the knee or elbow or (2) shock defined as a systolic blood pressure of less than 90 mm Hg or a heart rate greater than 120 beats per minute. EXPOSURES: Initiation of prehospital transfusion and time from MEDEVAC rescue to first transfusion, regardless of location (ie, prior to or during hospitalization). Transfusion recipients were compared with nonrecipients (unexposed) for whom transfusion was delayed or not given. MAIN OUTCOMES AND MEASURES: Mortality at 24 hours and 30 days after MEDEVAC rescue were coprimary outcomes. To balance injury severity, nonrecipients of prehospital transfusion were frequency matched to recipients by mechanism of injury, prehospital shock, severity of limb amputation, head injury, and torso hemorrhage. Cox regression was stratified by matched groups and also adjusted for age, injury year, transport team, tourniquet use, and time to MEDEVAC rescue. RESULTS: Of 502 patients (median age, 25 years [interquartile range, 22 to 29 years]; 98% male), 3 of 55 prehospital transfusion recipients (5%) and 85 of 447 nonrecipients (19%) died within 24 hours of MEDEVAC rescue (between-group difference, -14% [95% CI, -21% to -6%]; P = .01). By day 30, 6 recipients (11%) and 102 nonrecipients (23%) died (between-group difference, -12% [95% CI, -21% to -2%]; P = .04). For the 386 patients without missing covariate data among the 400 patients within the matched groups, the adjusted hazard ratio for mortality associated with prehospital transfusion was 0.26 (95% CI, 0.08 to 0.84, P = .02) over 24 hours (3 deaths among 54 recipients vs 67 deaths among 332 matched nonrecipients) and 0.39 (95% CI, 0.16 to 0.92, P = .03) over 30 days (6 vs 76 deaths, respectively). Time to initial transfusion, regardless of location (prehospital or during hospitalization), was associated with reduced 24-hour mortality only up to 15 minutes after MEDEVAC rescue (median, 36 minutes after injury; adjusted hazard ratio, 0.17 [95% CI, 0.04 to 0.73], P = .02; there were 2 deaths among 62 recipients vs 68 deaths among 324 delayed transfusion recipients or nonrecipients). CONCLUSIONS AND RELEVANCE: Among medically evacuated US military combat causalities in Afghanistan, blood product transfusion prehospital or within minutes of injury was associated with greater 24-hour and 30-day survival than delayed transfusion or no transfusion. The findings support prehospital transfusion in this setting.