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Estimating pre-impact and post-impact evacuation behaviors – An empirical study of hurricane Ida in coastal Louisiana and Mississippi
Abstract
Evacuation after hurricane impacts appear (post-impact evacuation) has been underemphasized in empirical
evacuation studies. This study uses well-examined factors for pre-impact evacuation and novel factors for post-impact evacuation in a sequential logit model for pre- and post-impact evacuation choices. Results show that the
evacuation warning is the only factor that affected both pre-impact and post-impact evacuations. Demographics
and housing characteristics are significant factors for pre-impact evacuation but not for post-impact evacuation,
while residential damages and durations of utility outages are significant situational factors for post-impact
evacuation. The durations of water and power outages had additive effects on the probability of evacuating
after hurricane impact. Based on the results, we argue that the conventional assumption that sheltered-in-place
residents will remain in the affected area, and the restoration planning and assistance generated with that
premise will not be aligned with the demand of residents facing inhabitable living situations with damaged
residences and prolonged utility outages. Agencies should consider extending the evacuation planning time
horizon for storm events likely to induce severe damages and outages and prepare for evacuation during disrupted conditions.
... There are comprehensive review papers on evacuation planning in the literature [14,15]. Although there are also many studies in the literature that analyze the evacuation decisions and behaviors of the people in notice disasters [16][17][18][19][20][21], there is a significant gap in the literature for no-notice disasters [22][23][24]. In the 1970s, the first evacuation studies were carried out on hurricanes [25,26]. ...
Following an earthquake, abnormal travel demand causes traffic congestion and poses significant problems for relief efforts. Research on post-earthquake travel demand is essential for disaster management. An effective disaster management strategy ensures achieving sustainable development goals. This study focused on this critical period and analyzed post-earthquake trip decisions. The city of Elazığ, a region not at risk of tsunami, was used as a case study. A 6.8 magnitude earthquake hit Elazığ in January 2020. After the earthquake, data from 2739 individuals were collected by a household survey conducted face-to-face. The data were segregated into two categories, depending on the earthquake’s intensity. The study used a binary logit model to examine the potential factors of trip decisions after an earthquake. The results showed that 75% of participants made at least one trip within 24 h after the earthquake. It was observed that household, building-and disaster-related attributes influence earthquake survivors’ trip decisions. The initial location at the time of the earthquake was the most significant factor affecting trip decisions. It was also found that individuals who experienced the earthquake outside their homes in both datasets were more likely to make a trip. Additionally, the dataset with higher earthquake intensity had more significant variables affecting the trip decision.
Critical infrastructure and public utility systems are often severely damaged by natural disasters like hurricanes. Based on a framework of household disaster resilience, this paper focuses on the role of utility disruption on household-level recovery in the context of Hurricane Sandy. Using data collected through a two-stage household survey, it first confirms that the sample selection bias is not present, thus the responses can be estimated sequentially. Second, it quantitatively examines factors contributing to hurricane-induced property damages and household-level recovery. The finding suggests that respondents who suffered from a longer period of utility disruptions (e.g., electricity, water, gas, phone/cell phone, public transportation) are more likely to incur monetary losses and have more difficulty in recovering. Effective preparedness activities (e.g., installing window protections, having an electric generator) can have positive results in reducing adverse shocks. Respondents with past hurricane experiences and higher educational attainments are found to be more resilient compared to others. Finally, the paper discusses the implications of the findings on effective preparation and mitigation strategies for future disasters.
Analysis of evacuation policies in eight southern U.S. coastal states found that all have laws to execute evacuation orders. However, only half have laws that related to community outreach, delivery of public education programs, and public notice programs. Policies that support hurricane evacuations that require informing those with limited English proficiency or persons with disabilities and functional needs of emergency evacuation plans can help minimize harm by protecting people at risk. Expanded communication efforts to the whole community through alerts and warnings and community outreach may result in fewer direct and indirect hurricane-related injuries and deaths.
A major challenge in the communication of spatial hazard information is the existence of substantial variation in people's ability to correctly infer the appropriate conclusions from hazard maps. Examination of the results from a variety of different types of hazard maps has identified patterns of erroneous as well as accurate processing of map information. In response, most hazard map studies have focused on the erroneous inferences and sought to develop displays that would cause hazard map viewers to make more accurate inferences about the hazard. This research has made some significant advances, but it has neglected the development and testing of spatial displays that can help people to make more timely and effective protective action decisions. This article concludes by summarizing the findings from hazard map research and recommending future research that will address these unresolved issues.
The objective of this paper is to empirically examine the impacts of infrastructure service disruptions on the well-being of vulnerable populations during disasters. There are limited studies that empirically evaluate the extent to which disruptions in infrastructure system services impact subpopulation groups differently and how these impacts relate to the wellbeing of households. Being able to systematically capture the differential experiences of sub-populations in a community due to infrastructure disruptions is necessary to highlight the differential needs and inequities that households have. In order to address this knowledge gap, this study derives an empirical relationship between sociodemographic factors of households and their subjective well-being impacts due to disruptions in various infrastructure services during and immediately after Hurricane Harvey. Statistical analysis driven by spearman-rank order correlations and fisher-z tests indicated significant disparities in well-being due to service disruptions among vulnerable population groups. The characterization of subjective well-being is used to explain to what extent infrastructure service disruptions influence different subpopulations. The results show that: (1) disruptions in transportation, solid waste, food, and water infrastructure services resulted in more significant well-being impact disparities as compared to electricity and communication services; (2) households identifying as Black and African American experienced well-being impact due to disruptions in food, transportation, and solid waste services; and (3) households were more likely to feel helpless, difficulty doing daily tasks and feeling distance from their community as a result of service disruptions. The findings present novel insights into understanding the role of infrastructure resilience in household well-being and highlights why it is so important to use approaches that consider various factors. Infrastructure resilience models tend to be monolithic. The results provide empirical and quantitative evidence of the inequalities in well-being impacts across various sub-populations. The research approach and findings enable a paradigm shift towards a more human-centric approach to infrastructure resilience.
In order to increase the flood resilience of cities (i.e., the ability to cope with flood hazards), it is also crucial to make critical infrastructure functions resilient, since these are essential for urban society. Cities are complex systems with many actors of different disciplines and many interdependent critical infrastructure networks and functions. Common flood risk analysis techniques provide useful information but are not sufficient to obtain a complete overview of the effects of flooding and potential measures to increase flood resilience related to critical infrastructure networks. Therefore, a more comprehensive approach is needed which helps accessing knowledge of actors in a structured way. Fort Lauderdale, Florida, United States has suffered from flood impacts, especially from disruptions in critical infrastructure. This paper shows how shared insight among different sectors and stakeholders into critical infrastructure resilience and potential resilience-enhancing measures was obtained using input from these actors. It also provides a first quantitative indication of resilience, indicated by the potential disruption due to floods and the effect of measures on resilience. The paper contributes to the existing literature on resilience specifically by considering the duration of disruption, the inclusion of critical infrastructure disruption in flood impact analysis, and the step from resilience quantification to measures.
Adaptation behaviour is of critical importance to reduce or avoid negative impacts of climate change. Many studies have examined which factors motivate individuals to adapt. However, a comprehensive overview of the key motivating factors of various adaptation behaviours is lacking. Here, we conduct a series of meta-analyses using data from 106 studies (90 papers) conducted in 23 different countries to examine how 13 motivational factors relate to various adaptation behaviours. Descriptive norms, negative affect, perceived self-efficacy and outcome efficacy of adaptive actions were most strongly associated with adaptive behaviour. In contrast, knowledge and experience, which are often assumed to be key barriers to adaptation, were relatively weakly related to adaptation. Research has disproportionally focused on studying experience and risk perception, flooding and hurricanes, and preparedness behaviours, while other motivational factors, hazards and adaptive behaviours have been understudied. These results point to important avenues for future research.
Adaptations to extreme climatic events like tropical storms are being built into disaster management by empowering vulnerable communities through activities like disaster awareness, trainings on rescue measures, provisions of better infrastructure, and strengthening of societal institutions under the Disaster Risk Reduction and Disaster Risk Management programs. With increasing threats from climate change, it is essential that the effectiveness of such measures is evaluated and limitations are addressed. The State of Odisha in Eastern India had witnessed nearly 10 years of such capacity building for cyclone management when it was hit by the severe cyclone “Phailin” in 2013. The public response to the evacuation order was overwhelming. In some areas, as many as 95% of the residents evacuated, and they were aware of the precautions to be taken before a storm strikes whereas some other areas showed as low as 33% evacuation and least interest in training and capacity building programs and maintenance of critical infrastructure like cyclone shelters. Analyzing evacuation responses with logistic regression, social economic issues like unemployment, prevalence of theft, and no provision for the evacuation of livestock to safety explained the evacuation failure significantly. In the future, such extreme events are predicted to hit coastal areas with more intensity due to climate change, and this necessitates that governments address such socio-economic problems along with cyclone adaptation programs to make disaster management more effective.
On September 10, 2017 Hurricane Irma turned off power for 6.7 million Florida electricity customers1. Despite the growing risk of U.S. electrical grid failure from increasing physical and cyber threats, rising demand, aging infrastructure, and a warming climate2, there is very little understanding of household vulnerability to this hazard. Based on 42 door-to-door interviews after the storm, our early results demonstrate typical patterns of vulnerability emerged in Irma related power failures. Residents relied on material, social, and intellectual resources to protect themselves and recover from the negative effects of the hurricane and blackout.
In August 2012, Hurricane Isaac made landfall twice in Louisiana. Cumulatively, over 1 million customers lost electricity as a result of the hurricane, some for more than 10 days. As a disaster, Hurricane Isaac is relatively unique because of the opportunity to largely isolate impacts and decisions directly associated with the electricity outage and restoration from cascading impacts related to direct flood or wind damage. Louisiana emergency managers, business representatives, and public officials were interviewed to develop and analyze a case study of the outage and restoration event. Interviewees were asked about (1) the significant impacts from the outage, (2) what (if any) lasting effects would result from the outage, (3) the relative performance of power restoration, (4) the effectiveness of communication between stakeholders, and (5) factors influencing the public and political perception of restoration performance. Other collected data included content from news media, government documents, press releases, situation reports, and publicly available quantitative data. Results suggest that there were few long-term impacts from the outage, and restoration performance was not unusually slow. Even so, the electricity service provider was the subject of vocal criticism. The findings of the case study highlight the importance of all aspects of communication and awareness raising before, during, and after power outages, as well as further research to facilitate this. The study reinforces the need to improve power continuity using a wide range of technical, political, and organizational strategies. Research and policy development is particularly needed for improving continuity of transportation, health care facilities, and gasoline provision. Understanding of the sociotechnical role of electricity outages within broader disaster contexts can only be improved through further interdisciplinary research.
TRB 2004 Annual Meeting CD-ROM Paper revised from original submittal. Fu and Wilmot Little attention has been given to estimating dynamic travel demand in transportation planning in the past. However, when factors which influence travel are changing significantly over time – such as with an approaching hurricane- dynamic demand and the resulting variation in traffic flow on the network become important. In the research reported in this paper, the decision to evacuate in the face of an oncoming hurricane is considered as a series of binary choices over time. A sequential binary logit model is developed to model the probability of a household evacuating at each time period before landfall as a function of the household’s socio-economic characteristics, the characteristics of the hurricane, and policy decisions made by authorities as the storm approaches. Data from Southwest Louisiana collected following Hurricane Andrew was used to estimate a model which produces dynamic travel demand estimates of hurricane evacuation. Based on the results, a sequential logit model appears capable of modeling dynamic evacuation demand satisfactorily.
Extreme climatic events are growing more severe and frequent, calling into question how prepared our infrastructure is to deal with these changes. Current infrastructure design is primarily based on precipitation Intensity-Duration-Frequency (IDF) curves with the so-called stationary assumption, meaning extremes will not vary significantly over time. However, climate change is expected to alter climatic extremes, a concept termed nonstationarity. Here we show that given nonstationarity, current IDF curves can substantially underestimate precipitation extremes and thus, they may not be suitable for infrastructure design in a changing climate. We show that a stationary climate assumption may lead to underestimation of extreme precipitation by as much as 60%, which increases the flood risk and failure risk in infrastructure systems. We then present a generalized framework for estimating nonstationary IDF curves and their uncertainties using Bayesian inference. The methodology can potentially be integrated in future design concepts.
Throughout human history, people have coped with, and adapted to, their environment. This accumulated capacity at local level is increasingly recognized to be critical in improving resilience and transformation. Nevertheless, city dwellers’ coping and adaptive practices are little known, poorly documented and often not taken into account in the work of municipal authorities and aid organizations. Against this background, this study provides a systematic overview of urban residents’ coping and adaptive practices, presents critical insights into their risk-reducing effects and discusses their role in the development of policies and projects to increase resilience. It shows that coping should not automatically be seen as maladaptive. The success or failure of urban societies in building resilience and moving towards transformation does not necessarily depend on the effectiveness of individual coping strategies but on the flexibility and inclusiveness of coping/adaptation systems at the individual, household and community level (i.e. the combined set of strategies). Therefore, it is crucial to support the ability of urban communities to negotiate their needs and rights in order to increase the flexibility and inclusiveness of these systems and make them more viable in today’s context.
An Anthropogenic Climate Change Index (ACCI) is developed and used to investigate the potential global warming contribution to current tropical cyclone activity. The ACCI is defined as the difference between the means of ensembles of climate simulations with and without anthropogenic gases and aerosols. This index indicates that the bulk of the current anthropogenic warming has occurred in the past four decades, which enables improved confidence in assessing hurricane changes as it removes many of the data issues from previous eras. We find no anthropogenic signal in annual global tropical cyclone or hurricane frequencies. But a strong signal is found in proportions of both weaker and stronger hurricanes: the proportion of Category 4 and 5 hurricanes has increased at a rate of ~25–30 % per °C of global warming after accounting for analysis and observing system changes. This has been balanced by a similar decrease in Category 1 and 2 hurricane proportions, leading to development of a distinctly bimodal intensity distribution, with the secondary maximum at Category 4 hurricanes. This global signal is reproduced in all ocean basins. The observed increase in Category 4–5 hurricanes may not continue at the same rate with future global warming. The analysis suggests that following an initial climate increase in intense hurricane proportions a saturation level will be reached beyond which any further global warming will have little effect.
Coastal northern Australia is largely owned and occupied by Aboriginal people who are strongly connected to their traditional country. We assess the views of Aboriginal people in Arnhem Land on the impacts of climate change and their possible precautionary responses to both sea level rise and a potential increase in the intensity of tropical cyclones in coastal communities. All respondents had heard about climate change, and 48 % had already seen environmental changes, particularly sea level rise, which they attributed to climate change. Fifty-eight percent of respondents would consider relocating in the future for safety reasons, although most respondents perceived living close to the sea as highly important for their future well-being, emphasising their strong connection to their traditional sea country. Many of those willing to relocate would consider moving inland, either temporarily or permanently, provided that community facilities could also be moved. Other respondents who said they would be unlikely to relocate in the future because of climate change impacts, and would prefer to adapt in situ with government support (e.g. building more shelters for severe cyclones, building sea walls and better roads for quick evacuation if necessary). We recommend that the diversity of adaptation preferences among Aboriginal people should be accommodated in policy to minimise social impacts of climate change and to take advantage of potential opportunities that could arise from moving.
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. DOI: 10.1061/(ASCE)NH.1527-6996.0000074. © 2012 American Society of Civil Engineers.
Hurricane Katrina created an unprecedented need for sheltering and temporary housing across a four-state area along the Gulf Coast. This article reviews the disaster literature with respect to sheltering and temporary housing and contrasts how these needs actually developed with respect to both the preimpact and postimpact evacuation situations. The article also investigates the ways that intergovernmental planning failed to anticipate the need for shelter/housing solutions or to implement effective measures to put those plans into operation.
Data from 23 studies confirm theoretical predictions that households’ adoption of earthquake hazard adjustments is correlated with their perceptions of the hazard and alternative adjustments, demographic characteristics, and social influences. However, some findings require modification of existing theories of hazard adjustment. Examination of the methods used in previous investigations underscores a need for better theories, more complete testing of existing theories, and improved data analytic and data reporting procedures in future tests of those theories.
Inferences on hurricane evacuation behavior are usually drawn through developing empirical models. These models are estimated using data that are specific to a given hurricane context. One important issue, therefore, is whether such models are applicable to different hurricane contexts. This paper investigates this transferability issue of evacuation choice models across different hurricanes. Initially, we estimate three separate models of the binary decision to evacuate or not, using data sets from three hurricanes (Andrew, Ivan, and Katrina) that occurred at different periods. Then a joint model is estimated combining these three evacuation data sources. When estimating the model jointly, the differences among the scale parameters of the data sets are specifically accounted for. The results from joint and separate models are then statistically tested to evaluate whether evacuation decision model parameters are transferable across different hurricane contexts. The result from the statistical test suggests that the parameters of the evacuation choice models are transferable over different hurricane contexts in similar regions, an important implication for policymakers and emergency preparedness agencies. DOI: 10.1061/(ASCE)TE.1943-5436.0000365. (C) 2012 American Society of Civil Engineers.
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.
This study evaluated the accuracy with which those at risk from Hurricane Bret were able to identify their hurricane risk areas. Overall, one-third of the respondents incorrectly reported their risk area and slightly more than half of these overestimated their risk. There was no systematic geographic pattern in the level of accuracy, but respondents with higher incomes and longer tenure on the coast were more likely to correctly report their risk areas. However, risk area accuracy was unrelated to the respondents' evacuation from the hurricane and length of coastal residence and hazard education were negatively related to evacuation. Implications for emergency managers are discussed.
Conventional evacuation studies typically do not gauge the development of participants’ certainty about evacuation-related decisions with the updates in the information provided to them. This study uses an online survey that provides three kinds of progressively varied information about the current status of a hypothetical hurricane for five days leading to its landfall and collects respondents’ certainty of their situational comprehension and evacuation-related decisions each day. Most participants (84%) made a final decision (60% evacuate) after seeing information of just one day (four days before the landfall), indicating a tendency of swift decision-making. Modeling shows that the time spent looking at information, especially uncertainty cone forecast maps, positively influences the understanding of the hurricane’s status, which in turn helps in increasing the certainty of making evacuation-related decisions, with an increasing temporal effect. This study contributes to the understanding of the public perception of information and its association with evacuation-related decision-making.
We systematically review and meta-analyze quantitative prediction models for hurricane evacuation decisions. Drawing on data from 33 prediction models and 29,873 households, we estimate distributions of effects on evacuation decisions for 25 predictors. Mobile home occupancy, evacuation orders, and having an evacuation plan showed the largest positive effects on evacuation, whereas increased age and Black race showed the largest negative effects. These results highlight the importance of both social-economic-structural factors and government action, such as evacuation orders, for enabling evacuation behaviors. Moderator analyses showed that models built using real-hurricane decisions showed larger effects than models of hypothetical decisions, especially for the strongest predictors. Additionally, models in Florida had more consistent results than for other U.S. states, and models with a larger number of covariates showed smaller effect sizes than models with fewer covariates. Importantly, our study improves methodologically and inferentially over previous reviews of this literature.
The objective of this paper is to develop household susceptibility indices related to disruptions in infrastructure services during disasters. The prolonged disruptions in infrastructure services create great hardship for the households of a community affected by natural hazards, especially shelter-in-place households who are at a high risk of such disruptions. The literature currently lacks tools and methods to assist decision makers in considering the tolerance and susceptibility of the household in communities to the infrastructure service disruptions. While there are some vulnerability indices in the disaster literature, the scores have not developed to specifically consider the risks associated with the infrastructure service disruptions. Hence, in this study, we developed and implemented Disruption Tolerance Index (DTI) to enable understanding of spatial patterns of susceptibility to service disruptions. In particular, we create susceptibility scores and maps for disruptions in power and transportation systems as two essential services on which households rely during their daily life. These service disruptions include power outages and road closures due to the natural hazards which disrupted households' access to these services. The factors for determining DTI scores are selected through an empirical analysis of households’ susceptibility to the disruptions, the scores are calculated by implementing Principal Components Analysis, and the scale of the scores is discussed. The study reveals the hot spots and cold spots of compound susceptibility to the disruptions. Unveiling the spatial clusters of susceptibility provides decision makers with the necessary information to consider the households' susceptibility in emergency plans and risk mitigation practices. The approach developed in this study could be adapted for different locations to enable understanding of the unique susceptibility of the households from the infrastructure services during disasters. Such understanding provides the decision-makers with the essential information to plan and mitigate the risk mitigation approaches and preparedness actions related to the risks associated with infrastructure service disruptions.
Shadow evacuation and non-compliance are among undesirable behaviors during hurricane events. Based on a post-Hurricane Matthew household survey, this study aims to understand the combined effects of information source and uncertainty on individual-level evacuate–stay decisions from the Jacksonville, Florida metropolitan area. A random parameter logit model is developed to capture the heterogeneous effects among individuals. The combined effects are substantial and distinct in the two cases and the study reveals several significant findings. First, consistent and sufficient warning information leads to better alignment with recommended actions. Second, larger social networks encourage compliance and induce shadow evacuation. Third, a highly diverse network induces compliance with the recommended actions, and isolated individuals should be provided with more information. Fourth, risk information from traditional media should be clearly delineated between the high-risk and low-risk areas for effective compliance. However, information from social media has a nonsignificant effect.
Compound flooding driven by multiple marine and hydrologic processes can lead to catastrophic impacts in densely populated coastal environments. Such impacts can propagate through a network of infrastructure systems causing cascading failures. In this study, a network-based framework is proposed to characterize compound flood risks in coastal environments. Pluvial, fluvial and coastal flood characteristics associated with Tropical Storm Matthew (2016) are simulated using a 2D hydrodynamic model over the Island of Saint Lucia. The complex network of infrastructure systems and the corresponding interdependencies are characterized by a risk assessment platform based on the Strongest Path Method (SPM). The model investigates the impacts of direct and indirect connections among infrastructure systems, explores their relative importance in the network and prioritizes the associated risks for more efficient resource allocation. The overall risks are assessed under non-flooding and flooding scenarios and the uncertainties are characterized by sensitivity analysis. Modelling results over Saint Lucia suggests that flood mitigation plans should prioritize the Hewanorra International Airport's backup electricity generators, runway, and taxiway/apron followed by transportation, electricity services, information and communication services. The proposed framework can be applied to study the impacts of compound flood risks on interdependent infrastructure systems in other regions around the world.
This paper provides empirical information for understanding the susceptibility of households to the infrastructure service disruptions caused by natural hazards. Understanding household-level susceptibility is critical to determine the risks associated with staying shelter-in-place during disasters. This information is essential for various stakeholders such as community leaders, emergency planners, and utility managers to prioritize and restore infrastructure services for the public. However, there is limited empirical information regarding the susceptibility of shelter-in-place households to the disruptions of different services. Hence, this study presents an exploratory analysis of empirical data collected from affected communities to identify the influencing factors of the households’ susceptibility. We utilized survey data collected from Hurricane Harvey (850 respondents), Hurricane Florence (362 respondents), and Hurricane Michael (583 respondents) to study the anatomy of susceptibility to eight infrastructure service disruptions. The descriptive analysis compared the similarities, such as rating the sewer and water systems as most important services, and differences, such as the varying levels of expectation for the service disruptions, of the three regions impacted by the disasters. Correlation analysis considered which underlying factors, including sociodemographic characteristics, protective actions and adjustments, previous experience and previous damage, social capital, and need for service of individual households along with the contextual and communal factors of the community, such as urbanization and previous disaster declarations, were associated with the ability of residents to respond to and withstand service disruptions. Although there were consistencies in the relationship of influencing factors to the level of susceptibility, the findings highlight that some variation in the influence of these factors was event-specific or service-specific. Finally, the contextual and communal factors of a community can bring unique insights to the anatomy of susceptibility to the service disruptions, as each location has inherent characteristics that would, directly and indirectly, influence households’ susceptibility to service disruptions. These findings provide the necessary empirical information to inform infrastructure prioritization decisions and emergency response actions to reduce the societal impacts of infrastructure service disruptions on vulnerable populations.
This paper presents a probabilistic model for assessing risk of cascading failures in co-located road and channel networks. The proposed Bayesian network analysis framework integrates network structural properties and empirical flood propagation data to model the spread of flooding. The model was tested in a multiple watershed scenario in Harris County, Texas (USA), using historical flood data from past events. The results show the capability of the proposed Bayesian network model to quantitatively characterize the failure (i.e., inundation) of road network considering the cascading failure (i.e., overflow) from the channel network. The proposed model also enables simulating the risk of flood cascades (i.e., flood propagation) on the road network with high accuracy. The generic design of the algorithm also enables the adaptation of the proposed framework in other cities and regions. Accordingly, the proposed model provides a new tool to help decision-makers prioritize infrastructure protection plans and emergency response actions.
With climate change increasingly affecting individuals’ day-to-day lives, interest is growing in the personal and household adaptation behaviors that people can engage in. Many of these behaviors focus on actions to protect oneself or one's household in response to immediate hazards rather than ones that may achieve longer-term adaptation goals. We conducted a content analysis of 75 publications identified through a systematic literature review to learn how researchers from a range of disciplines conceptualize adaptive behavior in the context of climate change and what kinds of specific actions they describe. Based on this review, we propose a comprehensive definition of personal and household adaptation behavior that considers its purpose (i.e., preventing harm or gaining benefits), timing (i.e., proactive or reactive), time scale (i.e., short-term or long-term), as well as who acts (i.e., the individual alone or with others) and who is affected by those actions (i.e., the individual, other people, or the environment). We classify specific individual adaptation behaviors into civic engagement, consumption, coping, household protection, learning, lifestyle changes, migration, and self-protection. Research is needed to better understand the personal and societal benefits of adaptation behaviors and how to more equitably support these actions in different contexts.
Large-scale disasters often trigger mass evacuation due to significant damages to urban systems. Understanding the evacuation and reentry (return) process of affected individuals is crucial for disaster management. Moreover, measuring the heterogeneity in the individuals' post-disaster behavior with respect to their socio-economic characteristics is essential for policy making. Recent studies have used large-scale location datasets collected from mobile devices to analyze post-disaster mobility patterns. Despite the availability of such data and the societal importance of the problem, no studies have focused on how income inequality affects the equity in post-disaster mobility. To overcome these research gaps, we overlay mobility data with income information from census to quantify the effects of income inequality on evacuation and reentry behavior after disasters, and the resulting spatial income segregation. Spatio-temporal analysis using location data of more than 1.7 million mobile phone users from Florida affected by Hurricane Irma reveal significant effects of income inequality on evacuation behavior. Evacuees with higher income were more likely to evacuate from affected areas and reach safer locations with less damage on housing and infrastructure. These differences were common among evacuees from both inside and outside mandatory evacuation zones. As a result of such effects of inequality, significant spatial income segregation was observed in the affected areas. Insights on the effects of income inequality on post-disaster mobility and spatial segregation could contribute to policies that better address social equity in pre-disaster preparation and post-disaster relief.
This research utilizes Bayesian network to address a range of possible risks to the electrical power system and its interdependent networks (EIN) and offers possible options to mitigate the consequences of a disruption. The interdependent electrical infrastructure system in Washington, D.C. is used as a case study to quantify the resilience
using the Bayesian network. Quantification of resilience is further analyzed based on different types of analysis such as forward propagation, backward propagation, sensitivity analysis, and information theory. The general insight drawn from these analyses indicate that reliability, backup power source, and resource restoration
are the prime factors contributed towards enhancing the resilience of an interdependent electrical infrastructure system.
The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
Two dynamic travel demand models for hurricane evacuation based on survival analysis are presented: a Cox proportional hazards model and a piecewise exponential model. These models were used to estimate the probability of a household's evacuating within discrete time intervals before hurricane landfall as a function of the household's socioeconomic characteristics, the characteristics of the hurricane, and management practice in terms of the issuance of an evacuation order. Data from southeast Louisiana collected following Hurricane Andrew were applied. Both models reproduced the evacuation behavior satisfactorily, although the piecewise exponential model was slightly more accurate. Both appear capable of modeling dynamic hurricane evacuation travel demand. However, the Cox model cannot accommodate certain time-dependent variables because of its structure. The piecewise exponential model does not have such a limitation. The piecewise exponential model also has the advantage of estimating the baseline hazards endogenously.
A dynamic hurricane evacuation travel demand model was estimated by using sequential logit with the data from Hurricane Floyd in South Carolina. The model was estimated on a random sample of 75% of the observations and applied to the remaining 25% as a test. In the test data, a total of 241 evacuations were predicted when 246 were observed, and the model estimated the number of evacuations in each 2-h period over 4 days with a root-mean-square error of 2.79 evacuations. Evacuation orders were modeled as a time-dependent variable. This significantly enhanced model performance over that achieved with evacuation orders as a stationary variable in previous work and provided the capability to analyze the impact of the type and timing of evacuation orders. That capability permits analyzing staged evacuation, in which areas are directed to evacuate in a sequence that optimizes network use. A model estimated on Hurricane Floyd evacuation data was transferred to Southeast Louisiana; its predictions were similar to evacuation behavior observed during Hurricane Andrew. With updating of the alternative specific constant of the transferred model to ensure the correct prediction of the total number of evacuations, the model predicted evacuation with a root-mean-square error of 4.53 evacuations per 6-h period. It was discovered that applying the same distance function to the two different hurricanes was a major source of error in model transfer. The representation of distance and its interactions with other variables need to be investigated further. The procedures and the information needed for model update warrant further study.
Population evacuation is an important component of emergency management planning for a variety of hazards, especially hurricanes and tropical storms that threaten coastal communities. This study examined previous evacuation decisions and evacuation intentions across 13 southeast Louisiana coastal parishes. Overall, the results indicate that most people will evacuate from strong storms, especially when ordered to do so. Future evacuation intentions correlated with previous evacuation decisions and corresponded to storm strength and official evacuation orders. Demographic factors had varying effects on behavior and intentions, with gender and race having the most consistent effects. The effects of income, education, homeownership, and housing type varied by storm strength and had different effects for intentions than previous behaviors. Previous flooding and wind damage had minimal effects on evacuation intentions. Risk perception, especially perception of the safety of one's own home, had strong effects on evacuation intentions. Qualitative results support the quantitative findings showing that people continue to rely on storm strength, especially Category or wind speed, as an indicator of risk and that persons who would not evacuate felt their homes were safe or had jobs that required them to report for duty. The results call for more research into how predictors of actual behaviors and intentions vary even while behaviors and intentions are correlated and how individuals determine that their house is safe.
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 extends previous research by testing the protective action decision model (PADM) on hurricane evacuation decisions during Hurricanes Katrina and Rita. An examination of this mediation model shows that a household’s evacuation decision, as predicted, is determined most directly by expected wind impacts and expected evacuation impediments. In turn, expected wind impacts and expected hydrological impacts are primarily determined by expected storm threat and expected rapid onset. Finally, expected storm threat, expected rapid onset, and expected evacuation impediments are determined by households’ personal characteristics, their reception of hurricane information, and their observations of social and environmental cues. These results are generally consistent with the PADM and reinforce the importance of testing multistage multiequation models of hurricane evacuation.
Hurricanes often threaten with catastrophic impacts on the lives of residents in the coastal areas of the United States. Timely evacuation limits this impact, but people may choose to evacuate or not during an extreme weather conditions due to differing personal constrains and environments that have little do with the direct risk. For example, during Hurricane Sandy a significant portion of New York and New Jersey residents facing potentially life-threatening storm-surge risk elected not to evacuate. While previous evacuation studies have investigated the complexities of hurricane behavior and revealed important factors impacting evacuation choice including the influence of social networks and information media, no quantitative analyses of social network effects on evacuation have been done. In some cases, evacuation decisions are solely based on personal obligations and needs, yet they can often be influenced by the people an individual frequently contacts. Previous sociological studies suggest that social networks serve the purpose of transmitting warning message by disseminating information about an impending threat and individuals having more social connections can be expected to receive more warning information. However, the empirical literature is inconclusive about how warnings received from social connections weigh into evacuation decision making. This study uses data obtained by interviewing people from high storm-surge risk areas to understand how they responded to Sandy. Individuals' ego-centric social network information was obtained by using the Personal Network Research Design (PNRD) approach. A mixed (random parameters) logit model of individual-level evacuation decision making is developed to explain the combined effects of individual, household, and social network characteristics along with the reliability of different information sources within a unified modeling framework. This model will enable emergency managers and planners to better predict evacuation demand: the number of individuals evacuating to a safe destination during a major hurricane threat. Researchers exploring different dimensions of evacuation logistics (for example, departure time, destination, modal split, route choice) and simulations may also find this study informative.
Maps are a sensible approach for communicating wildfire early warnings to the public as such warnings often contain a multitude of spatial information. However, a reluctance of agencies was found in using accurate and timely wildfire maps for public warnings, a sentiment potentially fuelled by beliefs that the public are not fluent map-readers and may be overwhelmed by the large amount of information. To test the validity of these beliefs, this study empirically compared the effectiveness of maps versus traditional text-based approaches for communicating spatial-related wildfire warning information. Through an online survey, 261 residents from wildfire prone areas in Western Australia were asked to view multidimensional spatial information regarding a simulated wildfire scenario presented as either text messages or maps, and were subsequently queried for their comprehension, their risk perceptions, and the attractiveness of the presentation format. Additionally, the survey captured the time required to interpret the varied information representations. The results showed that appropriately designed maps prevailed over text messages for the communication of most wildfire warning information by improving comprehension, elevating risk perceptions, and increasing appeal to the public. However, an optimal communication approach would be to couple map designs with several imperative textual descriptors. Especially, the textual description of safe shelters in the community (i.e. location names and addresses) yielded indispensible meaning when the locations were well-known landmarks, and hence should not be replaced by map-based depiction. Furthermore, several heuristics were identified to facilitate the design of effective warning maps across hazards in general.
One of the long-held assumptions of evacuation research is that households constitute the basic unit for decision making and ultimately evacuation itself. Most disaster researchers collect their data and build their models around the assumption that household decision making and ultimately evacuation are undertaken as a single unit. Recently it has been suggested that family and household evacuation patterns may be undergoing change and that there is an increasing trend of households using more than one vehicle to evacuate. This study addresses these potential changes directly by focusing on the issue of whether households actually stay together when evacuating versus splitting with groups leaving at different times. This investigation of households evacuating due to Hurricane Rita revealed that 9.3% of households evacuated in multiple groups at different times, with nearly 17% of households in highly vulnerable areas such as Galveston, Texas, splitting compared with 7.3% among shadow evacuee households. The findings suggest that location in highly vulnerable areas, concerns about reaching destinations safely, income, and having multiple vehicles were important determinants of splitting, with additional sociodemographic factors displaying marginal significance as well. Consequences for future research, modeling, and data collection are discussed.
A common theme in the literature on evacuation compliance is the result of largely social psychological perceptions of risk formed prior to taking the protective action. From this perspective, evacuation is a function of warning recipients corning to define themselves as in danger and believing that fleeing the immediate environment wilt reduce that danger. This paper explores the social psychological and social structural processes that result in such perceptions. In particular, attention is given to identifying perceptions that motivate evacuation, factors that direct perceptual outcomes and the ways in which motivation and perception are translated into action.
With rising sea levels and possible storm intensification due to climate change, current United States urban coastal flood management strategies will be challenged. Due to limitations of current flood management strategies, evacuation is likely to become increasingly prominent in many coastal areas. Thus it is important to think critically about challenges for successful evacuation planning, particularly for vulnerable communities. This paper brings together the evacuation planning, climate change and environmental justice literatures. We describe the unique challenges that environmental justice communities face with evacuation, and identify best practice guidelines to improve the quality of evacuation planning for these communities. The guidelines presented, while not comprehensive, provide a framework for planners and policymakers to consider when developing evacuation plans, both for current and future climate conditions, and could improve the quality of evacuation planning.
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.
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.
© 2015 Society for Risk Analysis.
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.
Hurricanes cause some of the worst traffic conditions, affecting evacuees' ability to reach safety before they are subjected to high winds, heavy rain, and flooding. This paper is one of the few to use a panel survey to examine similar household decisions over consecutive hurricanes. The study focuses on Hurricanes Ivan and Katrina, which were of similar strength and followed similar paths, and is fairly comprehensive in the number of traffic-related decisions considered. Contingency tables, binary logit models, and Goodman and Kruskal's gamma measure were used to examine the effects of previous decisions on (a) whether to evacuate, (b) day of departure, (c) destination type and location, (d) number of household vehicles taken, and (e) reason for route selection. Through the statistical analyses, it was discovered that (a) to a great extent, citizens made the same decision to evacuate or stay for Katrina as they did for Ivan, and higher incomes were not significant in changing that decision; (b) some evacuees departed earlier, but most evacuees departed on the last day possible; (c) most evacuees selected the same type of accommodations and made the same inside-the-county-or-parish or out-of-the-county-or-parish decisions in consecutive evacuations; (d) the number of household vehicles used in the evacuation did not decrease; and (e) route guidance as a selection criterion did not depend on previous evacuation experience.
This paper presents a review of highway-based evacuation modeling and simulation and its evolution over the past decade. The review includes the major components of roadway transportation planning and operations, including the current state of modeling in the forecasting of evacuation travel demand, distribution and assignment of evacuation demand to regional road networks to reach destinations, assignment of evacuees to various modes of transportation, and evaluation and testing of alternative management strategies to increase capacity of evacuation networks or manage demand. Although this discussion does not cover recent work in other modes used in evacuation such as air, rail, and pedestrian, this paper does highlight recent interdisciplinary modeling work in evacuation to help bridge the gap between the behavioral sciences and engineering and the application of emerging techniques for the verification, validation, and calibration of models. The manuscript also calls attention to special considerations and logistical difficulties, which have received limited attention to date. In addition to these concerns, the following future directions are discussed: further interdisciplinary efforts, including incorporating the medical community; using new technologies for communication of warnings and traffic condition information, data collection, and increased modeling resolution and confidence; using real-time information; and further model refinements and validation.