社区应急疏散动态协作调度优化研究

针对灾民数量和路网通行时间的动态性以及灾民疏散反应系数的随机性，本文提出了社区应急疏散协作调度优化流程，并以疏散灾民数量最大化和疏散成本最小化为目标，构建了社区应急疏散多种运输方式协作调度优化模型，并给出了求解该模型的改进多目标遗传算法。然后，论文使用Tansmodeler模拟社区应急疏散协作调度优化过程，加载疏散灾民动态需求和历史出行时间表，并对模型和算法进行验证。结果表明，该模型和算法可以在有效刻画疏散灾民数量和路网通行时间的基础上，为不同时刻的交通工具配置及疏散路径选取提供决策。     

Individual Actual or Perceived Property Flood Risk: Did it Predict Evacuation from Hurricane Isabel in North Carolina, 2003?

Individual perception of risk has consistently been considered an important determinant of hurricane evacuation in published studies and reviews. Adequate risk assessment is informed by environmental and social cues, as well as evacuation intentions and past disaster experience. This cross‐sectional study measured perceived flood risk of 570 residents of three coastal North Carolina counties, compared their perception with actual risk determined by updated flood plain maps, and determined if either was associated with evacuation from Hurricane Isabel in 2003. Census blocks were stratified by flood zone and 30 census blocks were randomly selected from each flood zone. Seven interviews were conducted at random locations within selected blocks. Bivariate and multivariable analyses were conducted to produce crude and adjusted risk differences. Neither the designated flood zone of the parcel where the home was located nor the residents' perceived flood risk was associated with evacuation from Hurricane Isabel in the bivariate analysis. In the multivariable analysis, intention to evacuate and home type were important confounders of the association between actual risk and evacuation. The belief that one is at high risk of property damage or injury is important in evacuation decision making. However, in this study, while coastal residents' perceived risk of flooding was correlated with their actual flood risk, neither was associated with evacuation. These findings provide important opportunities for education and intervention by policymakers and authorities to improve hurricane evacuation rates and raise flood risk awareness.     

Risk Perception and Disaster Preparedness in Immigrants and Canadian‐Born Adults: Analysis of a National Survey on Similarities and Differences

Research has documented that immigrants tend to experience more negative consequences from natural disasters compared to native‐born individuals, although research on how immigrants perceive and respond to natural disaster risks is sparse. We investigated how risk perception and disaster preparedness for natural disasters in immigrants compared to Canadian‐born individuals as justifications for culturally‐adapted risk communication and management. To this end, we analyzed the ratings on natural disaster risk perception beliefs and preparedness behaviors from a nationally representative survey (N = 1,089). Factor analyses revealed three underlying psychological dimensions of risk perception: external responsibility for disaster management, self‐preparedness responsibility, and illusiveness of preparedness. Although immigrants and Canadian‐born individuals shared the three‐factor structure, there were differences in the salience of five risk perception beliefs. Despite these differences, immigrants and Canadian‐born individuals were similar in the level of risk perception dimensions and disaster preparedness. Regression analyses revealed self‐preparedness responsibility and external responsibility for disaster management positively predicted disaster preparedness whereas illusiveness of preparedness negatively predicted disaster preparedness in both groups. Our results showed that immigrants’ risk perception and disaster preparedness were comparable to their Canadian‐born counterparts. That is, immigrant status did not necessarily yield differences in risk perception and disaster preparedness. These social groups may benefit from a risk communication and management strategy that addresses these risk perception dimensions to increase disaster preparedness. Given the diversity of the immigrant population, the model remains to be tested by further population segmentation.     

Factors Affecting Hurricane Evacuation Intentions

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.     

An Assessment of Change in Risk Perception and Optimistic Bias for Hurricanes Among Gulf Coast Residents

This study focuses on levels of concern for hurricanes among individuals living along the Gulf Coast during the quiescent two‐year period following the exceptionally destructive 2005 hurricane season. A small study of risk perception and optimistic bias was conducted immediately following Hurricanes Katrina and Rita. Two years later, a follow‐up was done in which respondents were recontacted. This provided an opportunity to examine changes, and potential causal ordering, in risk perception and optimistic bias. The analysis uses 201 panel respondents who were matched across the two mail surveys. Measures included hurricane risk perception, optimistic bias for hurricane evacuation, past hurricane experience, and a small set of demographic variables (age, sex, income, and education). Paired t‐tests were used to compare scores across time. Hurricane risk perception declined and optimistic bias increased. Cross‐lagged correlations were used to test the potential causal ordering between risk perception and optimistic bias, with a weak effect suggesting the former affects the latter. Additional cross‐lagged analysis using structural equation modeling was used to look more closely at the components of optimistic bias (risk to self vs. risk to others). A significant and stronger potentially causal effect from risk perception to optimistic bias was found. Analysis of the experience and demographic variables’ effects on risk perception and optimistic bias, and their change, provided mixed results. The lessening of risk perception and increase in optimistic bias over the period of quiescence suggest that risk communicators and emergency managers should direct attention toward reversing these trends to increase disaster preparedness.     

Impact Factors and Risk Analysis of Tropical Cyclones on a Highway Network

Coastal areas typically have high social and economic development and are likely to suffer huge losses due to tropical cyclones. These cyclones have a great impact on the transportation network, but there have been a limited number of studies about tropical‐cyclone‐induced transportation network functional damages, especially in Asia. This study develops an innovative measurement and analytical tool for highway network functional damage and risk in the context of a tropical cyclone, with which we explored the critical spatial characteristics of tropical cyclones with regard to functional damage to a highway network by developing linear regression models to quantify their relationship. Furthermore, we assessed the network's functional risk and calculated the return periods under different damage levels. In our analyses, we consider the real‐world highway network of Hainan province, China. Our results illustrate that the most important spatial characteristics were location (in particular, the midlands), travel distance, landfalling status, and origin coordinates. However, the trajectory direction did not obviously affect the results. Our analyses indicate that the highway network of Hainan province may suffer from a 90% functional damage scenario every 4.28 years. These results have critical policy implications for the transport sector in reference to emergency planning and disaster reduction.     

Who Would Be Willing to Accept Disaster Debris in Their Backyard? Investigating the Determinants of Public Attitudes in Post‐Fukushima Japan

In the wake of the 2011 Great East Japan Earthquake and tsunami, the Ministry of Environment in Japan asked municipalities nationwide to accept and treat disaster waste. This call for cross‐jurisdictional waste treatment provoked considerable public controversy. To explore how the national and municipal governments can seek more public acceptance in the wake of future disasters, this study implemented a nationwide survey and addressed the question of what factors influence the public's willingness to support their municipalities’ plans to host disaster waste. Three strands of the literature—on risk perception, public dissent, and prosocial behavior—offer valuable insights into hypothesis building, although none has addressed the above question. Estimates from an ordered logistic regression (N = 1,063) reveal that the conditions of a nuclear accident and living with a small child in the household would lower the level of support for accepting disaster waste, although this does not mean that people would give their support in the absence of a nuclear accident. The results also suggest that the national and municipal governments should communicate more with the public about the risks, benefits, and costs associated with hosting disaster debris, and make efforts to improve public trust in the national government.     

Evacuation from Natural Disasters: A Systematic Review of the Literature

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.     

The Impact of Perceived Flood Exposure on Flood‐Risk Perception: The Role of Distance

Natural hazards, such as major flood events, are occurring with increasing frequency and inflicting increasing levels of financial damages upon affected communities. The experience of such major flood events has brought about a significant change in attitudes to flood‐risk management, with a shift away from built engineering solutions alone towards a more multifaceted approach. Europe's experience with damaging flood episodes provided the impetus for the introduction of the European Floods Directive, requiring the establishment of flood‐risk management plans at the river‐basin scale. The effectiveness of such plans, focusing on prevention, protection, and preparedness, is dependent on adequate flood awareness and preparedness, and this is related to perception of flood risk. This is an important factor in the design and assessment of flood‐risk management. Whilst there is a modern body of literature exploring flood perception issues, there have been few examples that explore its spatial manifestations. Previous literature has examined perceived and real distance to a hazard source (such as a river, nuclear facility, landfill, or incinerator, etc.), whereas this article advances the literature by including an objectively assessed measure of distance to a perceived flood zone, using a cognitive mapping methodology. The article finds that distance to the perceived flood zone (perceived flood exposure) is a crucial factor in determining flood‐risk perception, both the cognitive and affective components. Furthermore, we find an interesting phenomenon of misperception among respondents. The article concludes by discussing the implications for flood‐risk management.     

Rural Nevada and Climate Change: Vulnerability,Beliefs, and Risk Perception

In this article, we present the results of a study investigating the influence of vulnerability to climate change as a function of physical vulnerability, sensitivity, and adaptive capacity on climate change risk perception. In 2008/2009, we surveyed Nevada ranchers and farmers to assess their climate change‐related beliefs, and risk perceptions, political orientations, and socioeconomic characteristics. Ranchers’ and farmers’ sensitivity to climate change was measured through estimating the proportion of their household income originating from highly scarce water‐dependent agriculture to the total income. Adaptive capacity was measured as a combination of the Social Status Index and the Poverty Index. Utilizing water availability and use, and population distribution GIS databases; we assessed water resource vulnerability in Nevada by zip code as an indicator of physical vulnerability to climate change. We performed correlation tests and multiple regression analyses to examine the impact of vulnerability and its three distinct components on risk perception. We find that vulnerability is not a significant determinant of risk perception. Physical vulnerability alone also does not impact risk perception. Both sensitivity and adaptive capacity increase risk perception. While age is not a significant determinant of it, gender plays an important role in shaping risk perception. Yet, general beliefs such as political orientations and climate change‐specific beliefs such as believing in the anthropogenic causes of climate change and connecting the locally observed impacts (in this case drought) to climate change are the most prominent determinants of risk perception.     

Local Spatial and Temporal Factors Influencing Population and Societal Vulnerability to Natural Disasters

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county‐level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county‐level social vulnerability. The results demonstrate that the dynamic characteristics of China's county‐level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering–scattering–gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.     

Evacuating People with Mobility‐Challenges in a Short‐Notice Disaster

In past disasters, arrangements have been made to evacuate people without their own transportation, requiring them to gather at select locations to be evacuated. Unfortunately, this type of plan does not help those people who are unable to move themselves to the designated meeting locations. In the United States, according to the Post‐Katrina Emergency Management Reform Act of 2006, state or local governments have the responsibility to coordinate evacuation plans for all populations. These include those with disabilities. However, few, if any, have plans in place for those who are mobility‐challenged. The problem of evacuating mobility‐challenged people from their individual locations in a short‐notice disaster is a challenging combinatorial optimization problem. In order to develop the model and select a solution approach, we surveyed related literature. Based on our review, we formulate the problem and develop an Ant Colony Optimization (ACO) algorithm to solve it. We then test two different versions of the ACO algorithm on five stylized datasets with several different parameter settings.     

Risk of Large‐Scale Evacuation Based on the Effectiveness of Rescue Strategies Under Different Crowd Densities

Crowd density is a key factor that influences the moving characteristics of a large group of people during a large‐scale evacuation. In this article, the macro features of crowd flow and subsequent rescue strategies were considered, and a series of characteristic crowd densities that affect large‐scale people movement, as well as the maximum bearing density when the crowd is extremely congested, were analyzed. On the basis of characteristic crowd densities, the queuing theory was applied to simulate crowd movement. Accordingly, the moving characteristics of the crowd and the effects of typical crowd density—which is viewed as the representation of the crowd's arrival intensity in front of the evacuation passageways—on rescue strategies was studied. Furthermore, a “risk axle of crowd density” is proposed to determine the efficiency of rescue strategies in a large‐scale evacuation, i.e., whether the rescue strategies are able to effectively maintain or improve evacuation efficiency. Finally, through some rational hypotheses for the value of evacuation risk, a three‐dimensional distribution of the evacuation risk is established to illustrate the risk axle of crowd density. This work aims to make some macro, but original, analysis on the risk of large‐scale crowd evacuation from the perspective of the efficiency of rescue strategies.     

Emergency medical center location problem with people evacuation solved by extended TODIM and objective programming

Journal of Combinatorial Optimization - This study aims to decide the location of emergency medical centers and the evacuation of survivors after a disaster happened. An extended interactive...     

How Do Disaster Characteristics Influence Risk Perception?

The main purpose of this study is to examine how risk perception is influenced by the type of disaster (flood or landslide) and victim characteristics. The data reported here are based on the National Risk Perception Survey (NRPS) that was administered for the victims and the general public in Taiwan in 2004. In that year, many towns in Taiwan were seriously affected by floods and landslides, resulting in huge economic losses and fatalities. The primary findings are: (1) the victims and the general public are concerned about the different potential hazards that might affect their residential area, (2) the negative associations between the sense of controllability and the perceived impact is high for landslide victims, but not for flood victims, and (3) disaster type, gender, and previously experienced disasters are good predictors of victims' attitudes toward natural disasters.     

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top‐down (or downscaling) approach to disaggregate administrative‐unit level asset value to grid‐cell level. To do so, finding the highly correlated “surrogate” indicators is the key. A combination of three data sets—nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc‐second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.     

Shannon Entropy for Quantifying Uncertainty and Risk in Economic Disparity

The rise in economic disparity presents significant risks to global social order and the resilience of local communities. However, existing measurement science for economic disparity (e.g., the Gini coefficient) does not explicitly consider a probability distribution with information, deficiencies, and uncertainties associated with the underlying income distribution. This article introduces the quantification of Shannon entropy for income inequality across scales, including national‐, subnational‐, and city‐level data. The probabilistic principles of Shannon entropy provide a new interpretation for uncertainty and risk related to economic disparity. Entropy and information‐based conflict rise as world incomes converge. High‐entropy instances can resemble both happy and prosperous societies as well as a socialist–communist social structure. Low entropy signals high‐risk tipping points for anomaly and conflict detection with higher confidence. Finally, spatial–temporal entropy maps for U.S. cities offer a city risk profiling framework. The results show polarization of household incomes within and across Baltimore, Washington, DC, and San Francisco. Entropy produces reliable results at significantly reduced computational costs than Gini coefficients.     

Determinants and Mapping of Collective Perceptions of Technological Risk: The Case of the Second Nuclear Power Plant in Taiwan

Nuclear power is a highly controversial and salient example of environmental risk. The siting or operating of a nuclear power plant often faces widespread public opposition. Although studies of public perceptions of nuclear power date back to 1970s, little research attempts to explain the spatial heterogeneity of risk attitude toward nuclear power among individuals or communities. This article intends to improve the knowledge about the major factors contributing to nuclear power plant risk perceptions by mapping the geographical patterns of local risk perception and examining the determinants in forming the nature and distribution of the perceived risk among potentially affected population. The analysis was conducted by a case study of the Second Nuclear Power Plant (SNPP) in Taiwan by using a novel methodology that incorporates a comprehensive risk perception (CRP) model into an ethnographic approach called risk perception mapping (RPM). First, we examined the determinants of local nuclear power risk perceptions through the CRP model and multivariate regression analysis. Second, the results were integrated with the RPM approach to map and explain the spatial pattern of risk perceptions. The findings demonstrate that the respondents regard the nuclear power plant as an extremely high‐risk facility, causing them to oppose the SNPP and reject the compensation payment to accept its continuing operation. Results also indicate that perceptions of nuclear power risk were mainly influenced by social trust, psychological and socioeconomic attributes, proximity, and the perceived effects of the SNPP on the quality of everyday life.     

The Return Period Analysis of Natural Disasters with Statistical Modeling of Bivariate Joint Probability Distribution

New features of natural disasters have been observed over the last several years. The factors that influence the disasters’ formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk‐based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis.