Evacuation Notice Research Articles

Information retrieval and classification of real-time multi-source hurricane evacuation notices

In the United States, tracking time-sensitive critical information such as evacuation notices for approaching natural hazards like hurricanes poses a significant challenge. This difficulty arises from the rapid issuing and distribution of these notices by numerous local authorities that may span multiple states. Additionally, evacuation notices often undergo frequent updates, and they are distributed through various online portals lacking standard formats. Unlike weather warnings, there is no centralized database for live (or past) evacuation notices. To meet the demand of data for crisis-related government notices, we developed an approach to detect and retrieve locally issued hurricane evacuation notices in real time. Evacuation-related text data were collected mainly through spatially targeted information retrieval. These data were then manually labeled and used to train natural language processing (NLP) models. The NLP models in turn classified all text data into one of the three notice categories, i.e., mandatory evacuation notices, voluntary evacuation notices, and not an evacuation notice. The classification of mandatory evacuation notices achieved a very high accuracy (recall = 96 %). These NLP models, when applied to future hurricanes, will provide real-time evacuation notices for situation awareness to higher-level government agencies and news outlets. The archived evacuation notices serve as a valuable resource for scholars to study government responses to weather warnings and individual behaviors influenced by evacuation history. Our research framework may extend to other disaster types, allowing for rapid and targeted retrieval, classification, redistribution, and archiving of real-time government orders and notifications.

Modelling electric vehicle charging network capacity and performance during short-notice evacuations

Electric vehicles (EVs) may add new challenges during mass evacuations. Understanding the magnitude of the impacts EVs may have during the pre-departure stage of mass evacuations is an essential first step when planning for mass evacuations in a future where EVs are more common. In this paper, a generalized framework based on a G/G/c/N queueing model (general arrival process, general service process, c charging stations, and N EVs) was developed to estimate the number of vehicles that can be charged in the pre-departure evacuation stage and thus assess the pre-departure impacts. The model outputs are the number of vehicles that have or have not been served during the evacuation period, as well as average queue times and maximum queue lengths. This model is tested using the current electric vehicle fleet and charging infrastructure of Prince George, British Columbia, as a case study with a hypothetical short notice forest fire scenario. It was found that for the present-day case of Prince George, there is not enough charging network capacity to service all vehicles before departure. Increasing the number of charging stations, providing earlier evacuation notices, and ensuring a balanced makeup of level 3 fast-charging of different types were all found to be effective in increasing the number of EVs that received adequate charging before departure.

Locate the warehouse: super distribution services

Learning outcomes The learning outcomes are as follows: to understand the strategic importance of location selection within the organization and how it influences other’s performance; to understand the impact of supply chain drivers on sales performance and to understand the role of other stakeholders in location selection strategy; to analyze the constraints in decision-making for selection of warehouse location and its remedies; to analyze the alternative options for warehouse in the light of top level management’s instruction or organizational strategies; to select the best possible alternate for long-term sustainability of warehouse location. Case overview/synopsis Ishaq Bashir, who has recently joined Super Distribution Services as Facility Manager, is indecisive in selecting a rental location for its offices and warehouse, either SDS had to move back to the old location or continue with existing warehouse facility. This existing location was selected by Ex Facility Manager around six months ago because SDS had to vacate their old location due to graving concern the owner of the premises. SDS had to search for a new facility and vacate the older premises within three months’ time as per “evacuation notice” received from the owner, and finally, the company shifted to the new location by 28 February 2017. After six months in September 2017, sales team realized a serious dip in sales by 1 per cent, and they claimed that this had happened due to wrong selection of the location of the warehouse which was far from the city. Now Bashir had to decide either to continue with the existing facility or move to the previous one or search for any third feasible location. Complexity academic level BBA and MBA. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 9: Operations and Logistics.

CSB issues update on Husky refinery explosion

The U.S. Chemical Safety & Hazard Investigation Board (CSB) has released a factual update and video animation of an April 2018 explosion and fire at the Husky Superior refinery in Superior, Wis. The incident occurred in the refinery’s fluid catalytic cracking unit at the start of a maintenance shutdown. A mixture of hydrocarbons and oxygen was released and ignited, leading to an explosion that sent debris 61 meters. Some of the debris punctured a large storage tank containing about 8,000 m3 of asphalt, spilling the hot liquid into the refinery. The asphalt caught fire and burned for nine hours. The fire generated potentially toxic smoke and risked compromising other process equipment containing hazardous chemicals, including hydrofluoric acid, used in the alkylation unit. An evacuation notice was issued covering a 5-km radius around the refinery plus a rectangle extending 16 km south. Overall, 36 people sought medical attention, including 11 refinery

Flood victims’ evacuation decisions: a semi-nonparametric estimation

PurposeThe purpose of this paper is to analyse the determinants of actual evacuation decisions of victims of the unprecedented 2014 year-end flood disaster which wreaked havoc across two east-coast states in Malaysia.Design/methodology/approachThe target population of this study is the group of victims affected by the December 2014 flood in the Malaysian east-coast states of Kelantan and Pahang. Sampling frames of the flood victims were obtained from the National Security Council offices of the two states. The empirical analysis of this paper is based on a unique data set obtained from a questionnaire survey of the flood victims. The final working sample consists of 372 respondents.FindingsImportant findings from this study are: victims who were given evacuation notices were five times more likely to evacuate, victims who participated in flood awareness programmes were less likely to move to evacuation centres, the further away victims’ homes were from the evacuation centres the more likely they were to evacuate, older victims were less likely to evacuate, larger households were more likely to evacuate, and victims with tertiary education were also less likely to evacuate.Originality/valueThis paper is unique because previous studies of Malaysian flood-related disasters are confined to floods of regular magnitude. This paper is also unique because it uses a semi-parametric estimation approach to obtain the marginal effects of the explanatory variables on evacuation decisions.

The simulation of urban-scale evacuation scenarios with application to the Swinley forest fire

Forest fires are an annual occurrence in many parts of the world forcing large-scale evacuation. The frequent and growing occurrence of these events makes it necessary to develop appropriate evacuation plans for areas that are susceptible to forest fires. The buildingEXODUS evacuation model has been extended to model large-scale urban evacuations by including the road network and open spaces (e.g. parks, green spaces and town squares) along with buildings. The evacuation simulation results have been coupled with the results of a forest fire spread model and applied to the Swinley forest fire which occurred in Berkshire, UK in May 2011. Four evacuation procedures differing in the routes taken by the pedestrians were evaluated providing key evacuation statistics such as time to reach the assembly location, the distance travelled, congestion experienced by the agents and the safety margins associated with using each evacuation route. A key finding of this work is the importance of formulating evacuation procedures that identifies the threatened population, provides timely evacuation notice, identifies appropriate routes that maintains a safe distance from the hazard front thereby maximising safety margins even at the cost of taking longer evacuation routes. Evacuation simulation offers a means of achieving these goals.

Modeling Emergency Managers’ Hurricane Evacuation Decisions

Emergency management and decision support system (EMDSS) tools play an important role in assisting emergency managers with making important decisions about the movement of people to safety when a jurisdiction is threatened by a storm. One of the important components of an EMDSS is an evacuation demand model that predicts whether and when households will evacuate when they are threatened by a storm. A critical input to that model is an emergency manager's decision to issue an evacuation notice. No existing mathematical models predict whether and when an emergency manager will issue an evacuation notice on the basis of a hurricane forecast and other contextual factors. To fill this gap, this research study sought to develop a model that would predict if and when an emergency manager would issue an evacuation notice when a jurisdiction was threatened by a storm. Data from poststorm assessment surveys and newspaper archives were used to retrieve past decisions made by evacuation managers for five storms in 45 coastal counties or parishes. The data were then used to develop a discrete choice model by use of the time-dependent sequential logit paradigm. Five independent predictor variables—storm surge, clearance time, time to landfall, hurricane category, and time of day—were found to be good predictors of the decisions made by emergency managers. This model could be useful to emergency managers to estimate how other emergency managers decide to evacuate an area when they are faced with an evacuation decision. The model could also benefit researchers and practitioners engaged in modeling and understanding hurricane evacuation behavior.

A statistical analysis of the dynamics of household hurricane-evacuation decisions

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, socio-economic characteristics, actions by authority, and geographic location, have been considered along with the dynamic nature of the hurricane itself. In addition, taking the landfall as a base, how the evacuation timing varies, considering both the time-of-day and hours before landfall, has been analyzed rigorously. Influential factors in the joint model include the relative time until the hurricane’s landfall, height of the coastal flooding, and approaching speed of the hurricane; household’s geographic location (state); having more than one child in the household, vehicle ownership, and level of education; and type of evacuation notice received (voluntary or mandatory). Two time intervals from 30 to 42 h and 42 to 66 h before landfall resulted in random parameters, reflecting mixed effects on the likelihood to evacuate/stay. Possible sources of the unobserved heterogeneity captured by the random parameters include the respondents’ risk perception or other unobserved physiological and psychological factors associated with how respondents comprehend a hurricane threat. Thus, the model serves the purpose of estimating evacuation decision and timing simultaneously using the data of Hurricane Ivan.

Modeling Shadow Evacuation for Hurricanes with Random-Parameter Logit Model

Shadow evacuation, defined as people evacuating from outside the official evacuation zone, can be extensive, can cause congestion, and can make it difficult for evacuees in high-risk areas to reach safety. For modeling and simulation, approaches typically involve selecting a distance from evacuation zones and a percentage of households evacuating within that distance. In previous studies, the distance and participation rate ranges were significant; this aspect created a need for better understanding and modeling of shadow evacuation. A model that can capture shadow evacuation could help reduce the subjectivity associated with previous approaches. This study accounts for stochastic responses to evacuation notices through a random-parameter binary logit model based on Hurricane Ivan survey data. Random coefficients are associated with variables representing the receipt of nonmandatory evacuation notices and the absence of evacuation notices. The statistical significance of the random coefficients explains the shadow evacuation phenomenon. Other sociodemographic, economic, and evacuation preparation factors, consistent with previous literature, also influence a household’s decision to evacuate. This study suggests that households that do not receive an evacuation notice are generally less likely to evacuate. In recognition of the fact that households’ perceived risk is related to their distance to the coast, the likelihood of evacuating for households that do not receive an evacuation notice decreases as their distances to the coast increase on average. A distance sensitivity factor is introduced to construct scenarios of the geographical extent of shadow evacuation, which can be used to understand the impact of shadow evacuation on clearance time.

Evaluation of the predictive capacity of dead fuel moisture models for Eastern Australia grasslands

The moisture content of dead grass fuels is an important input to grassland fire behaviour prediction models. We used standing dead grass moisture observations collected within a large latitudinal spectrum in Eastern Australia to evaluate the predictive capacity of six different fuel moisture prediction models. The best-performing models, which ranged from a simple empirical formulation to a physically based process model, yield mean absolute errors of 2.0% moisture content, corresponding to a 25–30% mean absolute percentage error. These models tended to slightly underpredict the moisture content observations. The results have important implications for the authenticity of fire danger rating and operational fire behaviour prediction, which form the basis of community information and warnings, such as evacuation notices, in Australia.

A random parameter ordered probit model to understand the mobilization time during hurricane evacuation

This paper presents a random parameters ordered probit model to capture underlying unobserved characteristics in the timing behavior of the evacuees that elapses in between their evacuation decision and actual evacuation i.e. the mobilization time for an evacuee. The ordered probit model has been developed by using Hurricane Ivan data and the estimation findings suggest that the mobilization time involves a complex interaction of variables related to household location, evacuation characteristics, and socio-economic characteristics among others. In the model, six variables- source and time of evacuation notice received, work constraint, previous hurricane experience, race and income- were found to be random and the random parameters (all normally distributed) suggest that their effect varies across the observations. In addition, the model introduces some new factors that impact the mobilization time (for example, the mobilization time for evacuees evacuating to public shelters is significantly lower) which have not been found in the earlier literature to the best of our knowledge. The findings of this study are useful to determine different fractions of people evacuating early or delaying for some time once they actually decide to evacuate, for a given socio-demographic profile. These fractions can be used in the future to develop more accurate dynamic travel demands for use in traffic simulation models.

Empirical Evacuation Response Curve during Hurricane Irene in Cape May County, New Jersey

Understanding evacuation response behavior is critical for public officials in deciding when to issue emergency evacuation orders for an impending hurricane. Such behavior is typically measured by an evacuation response curve that represents the proportion of total evacuation demand over time. This study analyzes evacuation behavior and constructs an evacuation response curve on the basis of traffic data collected during Hurricane Irene in 2011 in Cape May County, New Jersey. The evacuation response curve follows a general S-shape with sharp upward changes in slope after the issuance of mandatory evacuation notices. These changes in slope represent quick response behavior, which may be caused in part by an easily mobilized tourist population, lack of hurricane evacuation experience, or the nature of the location, in this case a rural area with limited evacuation routes. Moreover, the widely used S-curves with different mathematical functions and the state-of-the-art behavior models are calibrated and compared with empirical data. The results show that the calibrated S-curves with logit and Rayleigh functions fit empirical data better. The evacuation behavior analysis and calibrated evacuation response models from this hurricane evacuation event may benefit evacuation planning in similar areas. In addition, traffic data used in this study may also be valuable for the comparative analysis of traffic patterns between the evacuation periods and regular weekdays and weekends.

Communication’s Role and Technology Preferences during Hurricane Evacuations

Emergency evacuations are dynamic, time-critical events involving complex coordination decisions. To develop robust evacuation policies, it is important to understand how people respond to evacuation alerts, including choices of time to leave and routes to take. The purpose of this study was to investigate the preferred communication sources and modalities for evacuees to use and learn about changes in hurricane scenarios. A hurricane evacuation behavior survey was administrated to residents in the greater Charleston, South Carolina, area. In this study, two hurricane scenarios are examined to understand prehurricane evacuation decisions and communication preferences of residents. Results from the survey indicate that while driving, respondents prefer using radios to gather traffic and weather information. When at home, television is the most preferred modality among respondents to determine weather status and to find out about hurricane evacuation notices. Concerning evacuation behavior, respondents prefer leaving in the daytime rather than at night. To account for this preference, evacuation announcements and notifications should be broadcast early enough to allow for more daytime evacuations. This may result in a longer lead time if the announcement would have otherwise occurred at night. The evacuation timing decisions from the survey are then compared with historical behavior data from a retrospective Hurricane Hugo survey. The respondents indicate a tendency to evacuate earlier than what was shown in the Hurricane Hugo survey. This may imply that it takes longer to actually prepare to leave than respondents to the current survey realize. Alternatively, respondents to the Hurricane Hugo survey may pay more attention to weather patterns as well as wait for orders to be provided by the state rather than make a proactive decision to leave.

Household-Level Model for Hurricane Evacuation Destination Type Choice Using Hurricane Ivan Data

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.

A random-parameter hazard-based model to understand household evacuation timing behavior

The goal of this paper is to develop a random-parameter hazard-based model to understand hurricane evacuation timing by individual households. The choice of departure time during disasters is a complex dynamic process and depends on the risk that the hazard represents, the characteristics of the household and the built environment features. However, the risk responses are heterogeneous across the households; this unobserved heterogeneity is captured through random parameters in the model. The model is estimated with data from Hurricane Ivan including households from Alabama, Louisiana, Florida and Mississippi. It is found that the variables related to household location, destination characteristics, socio-economic characteristics, evacuation notice and household decision making are key determinants of the departure time. As such the developed model provides some fundamental inferences about hurricane evacuation timing behavior.

Why Don't Coastal Residents Choose to Evacuate for Hurricanes?

A multinomial choice framework was used to analyze data from hypothetical storm forecast scenarios administered via mail survey to a random sample of U.S. Gulf Coast residents. Results indicate that the issuance of a mandatory evacuation notice and the presence of higher wind speeds had the largest influence on increasing the likelihood of evacuation. Age, race, disability, distance, and education were significant in explaining one's decision to wait relative to choosing to evacuate. Blacks and disabled individuals were strictly less likely to wait and more likely to make an immediate evacuation decision. Hurricane Katrina evacuees and those with an evacuation destination identified were also more likely to decide to evacuate, but were also more likely to wait before deciding. Results indicate that residents of mobile homes were more likely to either evacuate or wait before making a decision, but strictly less likely not to evacuate. Respondents very confident in being rescued were strictly more likely not to evacuate. Results indicate that not having an evacuation destination identified was the most influential factor regarding the likelihood of not knowing what choice to make.

Modeling the Hurricane Evacuation Response Curve

The objective of this study is to develop a hurricane evacuation response curve that is sensitive to the characteristics of the hurricane, the time of day, and the type and timing of evacuation notice issued. Two data sets from past hurricanes, Floyd in South Carolina and Andrew in southeastern Louisiana, were used for model development and testing. A model developed on the Hurricane Floyd data set produced plausible results when it was tested with a series of storm scenarios and different evacuation notice policies. The same model predicted evacuation response behavior for Hurricane Andrew that was similar to observed behavior.

Risk factors for pet evacuation failure after a slow-onset disaster.

To determine risk factors for pet evacuation failure during a flood. Cross-sectional survey. 203 pet-owning households in a flooded region. Persons under evacuation notice because of a flood were interviewed by use of a random telephone survey. 102 households evacuated with their pets, whereas 101 households evacuated without their pets. Low pet attachment and commitment scores were significantly associated with a greater chance of pet evacuation failure. Risk of pet evacuation failure and lower attachment and commitment scores were also associated with pet management practices prior to the disaster, such as dogs being kept outdoors most of the time or owners not having carriers for their cats. More than 90% of owners made housing arrangements for their pets without assistance. Predictors of pet evacuation failure are usually present before a disaster strikes and are potentially modifiable. Mitigation of pet evacuation failure should focus on activities that reinforce responsible pet ownership and strengthen the human-animal bond, including socializing dogs, attending dog behavior training classes, transporting cats in nondisaster times, and seeking regular preventive veterinary care. Most pet owners are self-reliant in disasters, and this behavior should be encouraged.

Human and Pet-related Risk Factors for Household Evacuation Failure During a Natural Disaster

This study characterized risk factors for household evacuation failure. A random digit dial telephone survey was conducted of 397 households in Yuba County, California, in July 1997, 6 months after residents had been under evacuation notice due to flooding. Case households failed to evacuate, whereas control households evacuated. The cumulative incidence of household evacuation failure was 19.4%. Fewer households with children (25.8%) failed to evacuate than households without children (45.9%, p < 0.01). More households with pets (20.9%) than households without pets failed to evacuate (16.3%, p = 0.11). With multivariate logistic regression, the risk of household evacuation failure was lower in households with children (odds ratio = 0.4, 95% confidence interval: 0.2, 0.8) compared with households without children. The risk of household evacuation failure increased in pet-owning households without children (odds ratio = 1.3, 95% confidence interval: 1.1, 1.5) compared with pet-owning households with children; the more pets a household owned, the higher the risk of household evacuation failure was. Impediments to pet evacuation, including owning multiple pets, owning outdoor dogs, or not having a cat carrier, explained why many households that owned pets failed to evacuate. Predisaster planning should place a high priority on facilitating pet evacuation through predisaster education of pet owners and emergency management personnel.

A Study of Pet Rescue in Two Disasters

Pet rescues endanger public and animal health in disasters and are a direct consequence of pet evacuation failure. This study characterized pet rescue attempts in two disasters. A random digit dial telephone survey was conducted of 397 households in Yuba County, California, where residents were under an evacuation notice due to flooding. A mail survey was conducted of 241 households in Weyauwega, Wisconsin, where residents evacuated from a hazardous chemical spill. Risk factors for pet rescue were identified using multivariate logistic regression. Case households were defined as those that evacuated without pets and later attempted to rescue them, while control households were those that evacuated without their pet and did not attempt a rescue. Approximately 20 percent and 50 percent of pet-owning households that evacuated failed to take their pet with them in Yuba County and Weyauwega, respectively. Approximately 80 percent of persons who reentered the evacuated area did so to rescue their pet. Attempts to rescue a pet was most common by households with children. Predisaster planning should, therefore, place a higher priority on facilitating pet evacuation so as to minimize the subsequent need to rescue pets.