An improved online evacuation strategy from a convex region on grid networks

This study considers an evacuation problem where an evacuee tries to escape to the boundary of a convex affected area on a grid network. The boundary is unknown to the evacuee and cannot be identified until reaching an arbitrary point of it. We propose an online evacuation strategy with a competitive ratio less than 17.5, which improves the previously best result of 19.5.     

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。     

Possibilistic scheduling routing for short-notice bushfire emergency evacuation under uncertainties: An Australian case study

This paper aims to develop a capacitated vehicle routing solution to evacuate short-notice evacuees with time windows and disruption risks under uncertainties during a bushfire. A heuristic solution technique is applied to solve the triangular possibilistic model to optimise emergency delivery service. The effectiveness of the proposed algorithm is evaluated by comparing it with a designed genetic algorithm on sets of 20 numerical examples. The model is then applied to the real case study of 2009 Black Saturday bushfires in Victoria, Australia. The results show that it is possible to transfer the last-minute evacuees during the Black Saturday bushfires under the hard time window constraint. Network disruptions however have impact on resource utilisation. The modelling outputs will be useful in the development of emergency plans and evacuation strategies to enhance rapid response to last-minute evacuation in a bushfire emergency.     

Spatial and Temporal Variation in Evacuee Risk Perception Throughout the Evacuation and Return‐Entry Process

Developing effective evacuation and return‐entry plans requires understanding the spatial and temporal dimensions of risk perception experienced by evacuees throughout a disaster event. Using data gathered from the 2008 Cedar Rapids, Iowa Flood, this article explores how risk perception and location influence evacuee behavior during the evacuation and return‐entry process. Three themes are discussed: (1) the spatial and temporal characteristics of risk perception throughout the evacuation and return‐entry process, (2) the relationship between risk perception and household compliance with return‐entry orders, and (3) the role social influences have on the timing of the return by households. The results indicate that geographic location and spatial variation of risk influenced household risk perception and compliance with return‐entry plans. In addition, sociodemographic characteristics influenced the timing and characteristics of the return groups. The findings of this study advance knowledge of evacuee behavior throughout a disaster and can inform strategies used by emergency managers throughout the evacuation and return‐entry process.     

EvacuAid: A Probabilistic Model to Determine the Expected Loss of Life for Different Mass Evacuation Strategies During Flood Threats

Evacuation of people in case of a threat is a possible risk management strategy. Evacuation has the potential to save lives, but it can be costly with respect to time, money, and credibility. The consequences of an evacuation strategy depend on a combination of the time available, citizen response, authority response, and capacity of the infrastructure. The literature that discusses evacuations in case of flood risk management focuses, in most cases, only on a best‐case strategy as a preventive evacuation and excludes other possible strategies. This article introduces a probabilistic method, EvacuAid, to determine the benefits of different types of evacuation with regards to loss of life. The method is applied for a case study in the Netherlands for preventive and vertical evacuation due to flood risk. The results illustrate the impact of uncertainties in available time and actual conditions (e.g., the responses of citizens and authorities and the use of infrastructure). It is concluded that preparation for evacuation requires adaptive planning that takes preventive and vertical evacuation into account, based on a risk management approach.     

Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events

Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this article, a bilevel mathematical optimization model is formulated to address this issue, and our research objective is to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow (also referred to as lane reversal) and crossing elimination at intersections. Mathematical models are developed to represent the two reconfiguration schemes and characterize the interactions between traffic operators and passengers. Specifically, traffic operators act as leaders to determine the optimal system reconfiguration to minimize the total travel time for all the users (both evacuees and regular commuters), while passengers act as followers by freely choosing the path with the minimum travel time, which eventually converges to a user equilibrium state. For each given network reconfiguration, the lower‐level problem is formulated as a traffic assignment problem (TAP) where each user tries to minimize his/her own travel time. To tackle the lower‐level optimization problem, a gradient projection method is leveraged to shift the flow from other nonshortest paths to the shortest path between each origin–destination pair, eventually converging to the user equilibrium traffic assignment. The upper‐level problem is formulated as a constrained discrete optimization problem, and a probabilistic solution discovery algorithm is used to obtain the near‐optimal solution. Two numerical examples are used to demonstrate the effectiveness of the proposed method in restoring the traffic system performance.     

Perceptions of Risk and Vulnerability Following Exposure to a Major Natural Disaster: The Calgary Flood of 2013

Many studies have examined the general public's flood risk perceptions in the aftermath of local and regional flooding. However, relatively few studies have focused on large‐scale events that affect tens of thousands of people within an urban center. Similarly, in spite of previous research on flood risks, unresolved questions persist regarding the variables that might influence perceptions of risk and vulnerability, along with management preferences. In light of the opportunities presented by these knowledge gaps, the research reported here examined public perceptions of flood risk and vulnerability, and management preferences, within the city of Calgary in the aftermath of extensive flooding in 2013. Our findings, which come from an online survey of residents, reveal that direct experience with flooding is not a differentiating factor for risk perceptions when comparing evacuees with nonevacuees who might all experience future risks. However, we do find that judgments about vulnerability—as a function of how people perceive physical distance—do differ according to one's evacuation experience. Our results also indicate that concern about climate change is an important predictor of flood risk perceptions, as is trust in government risk managers. In terms of mitigation preferences, our results reveal differences in support for large infrastructure projects based on whether respondents feel they might actually benefit from them.     

An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 2—Hazard Modeling

Hurricane track and intensity can change rapidly in unexpected ways, thus making predictions of hurricanes and related hazards uncertain. This inherent uncertainty often translates into suboptimal decision-making outcomes, such as unnecessary evacuation. Representing this uncertainty is thus critical in evacuation planning and related activities. We describe a physics-based hazard modeling approach that (1) dynamically accounts for the physical interactions among hazard components and (2) captures hurricane evolution uncertainty using an ensemble method. This loosely coupled model system provides a framework for probabilistic water inundation and wind speed levels for a new, risk-based approach to evacuation modeling, described in a companion article in this issue. It combines the Weather Research and Forecasting (WRF) meteorological model, the Coupled Routing and Excess STorage (CREST) hydrologic model, and the ADvanced CIRCulation (ADCIRC) storm surge, tide, and wind-wave model to compute inundation levels and wind speeds for an ensemble of hurricane predictions. Perturbations to WRF's initial and boundary conditions and different model physics/parameterizations generate an ensemble of storm solutions, which are then used to drive the coupled hydrologic + hydrodynamic models. Hurricane Isabel (2003) is used as a case study to illustrate the ensemble-based approach. The inundation, river runoff, and wind hazard results are strongly dependent on the accuracy of the mesoscale meteorological simulations, which improves with decreasing lead time to hurricane landfall. The ensemble envelope brackets the observed behavior while providing “best-case” and “worst-case” scenarios for the subsequent risk-based evacuation model.     

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.     

People's Risk Recognition Preceding Evacuation and Its Role in Demand Modeling and Planning

Evacuation planning and management involves estimating the travel demand in the event that such action is required. This is usually done as a function of people's decision to evacuate, which we show is strongly linked to their risk awareness. We use an empirical data set, which shows tsunami evacuation behavior, to demonstrate that risk recognition is not synonymous with objective risk, but is instead determined by a combination of factors including risk education, information, and sociodemographics, and that it changes dynamically over time. Based on these findings, we formulate an ordered logit model to describe risk recognition combined with a latent class model to describe evacuation choices. Our proposed evacuation choice model along with a risk recognition class can evaluate quantitatively the influence of disaster mitigation measures, risk education, and risk information. The results obtained from the risk recognition model show that risk information has a greater impact in the sense that people recognize their high risk. The results of the evacuation choice model show that people who are unaware of their risk take a longer time to evacuate.     

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.     

The mixed evacuation problem

A dynamic network introduced by Ford and Fulkerson is a directed graph with capacities and transit times on its arcs. The quickest transshipment problem is one of the most fundamental problems in dynamic networks. In this problem, we are given sources and sinks. Then the goal of this problem is to find a minimum time limit such that we can send the right amount of flow from sources to sinks. In this paper, we introduce a variant of this problem called the mixed evacuation problem. This problem models an emergent situation in which people can evacuate on foot or by car. The goal is to organize such a mixed evacuation so that an efficient evacuation can be achieved. In this paper, we study this problem from the theoretical and practical viewpoints. In the first part, we prove the polynomial-time solvability of this problem in the case where the number of sources and sinks is not large, and also prove the polynomial-time solvability and computational hardness of its variants with integer constraints. In the second part, we apply our model to the case study of Minabe town in Wakayama prefecture, Japan.     

Creating a sense of mission

Mission is still a relatively neglected area of management, and there is no clear agreement on what it encompasses. The Ashridge Strategic Management Centre conducted a 2-year research project designed to fill this gap. The research found that if mission is more clearly defined it can be managed better, and developed a model of mission that includes four elements--purpose, strategy, behaviour standards and values. The project identified companies where, in addition to strong links between these elements, employees also showed an emotional commitment to their company which Campbell has called a 'sense of mission'. This commitment was deepest when there was a match between the employee's values and the company's values.     

Flood Risk Acceptability and Economic Value of Evacuation

The economic value of evacuation and its relationship with flood risk acceptability in Japan were studied by applying the contingent valuation method (CVM). Flood risk acceptability here refers to the extent to which people accept the occurrence of floods, in terms of scale and frequency. The economic value of evacuation refers to people's willingness to pay (WTP) for avoiding evacuation inconvenience because of its inconvenience and the potential for certain losses as a result of evacuation. Our main finding was that over half of the people (56%) who actually evacuated in a real flood situation reported inconvenience. The greatest inconveniences were the shortages of information and food. Evacuation inconvenience can be regarded as an important factor causing the low rate of evacuation in Japan. The WTP for avoiding current inconvenience was approximately half of the estimated economic value of evacuation, implying that the current budget for evacuation is too small and should be increased to improve the conditions of evacuation sites. The economic value of evacuation can be taken into consideration in the risk assessment process in order to evaluate the efficiency of risk reduction measures. Flood risk acceptability and home ownership are two major statistically significantly determinants of the WTP. Considering that those who accept flood risk have a lower WTP for flood risk control (ex ante measures) than those who reject it, it is reasonable to think that there may be a tradeoff between the public WTPs for ex ante or ex post measures.     

两级供应链中订货延迟的影响分析

基于报童模型,在1个供应商和2个零售商组成的供应链系统中,建模分析了零售商实行订货延迟策略对自身、供应商和供应链系统的利润影响,得出了它们各自的订货时机偏好,并根据服务水平描绘了供应商和零售商的订货偏好区域分布图,由此得到零售商订货时的Pareto最优区域。研究结果表明,在通常情况下供应商希望零售商预订,而零售商喜欢延迟订货,这和现实情况是相符的。结果还表明,在某些情况下零售商延迟订货对自身有利,但对系统不利;当供应商服务水平较高时,零售商延迟订货对自身、对系统都有利。     

基于双层规划的交通疏散中车辆出发与交通控制综合优化

在有组织的区域性疏散中,从需求调节(即疏散车辆出发安排)和供给管理(即交通管控)两方面对疏散交通流进行合理组织,是提高疏散效率的有效途径。论文立足于疏散车辆出发组织与路网交通管控之间的双层决策关系,建立双层规划模型对集结点疏散车辆的发车频率、路线和交叉口控制参数进行综合优化,其中上层模型通过优化信号交叉口的相位绿灯时间即绿信比以降低平均延误,其决策影响到交叉口通行能力等供给特性;下层模型通过优化疏散车辆的分批出发时间与路线以压缩疏散总时间,其决策影响到交叉口流量等需求特性。设计了基于遗产算法的求解步骤,给出了一个数值算例。将模型优化方案和只从交叉口控制参数出发的单方面优化模式所得结果进行了比较,结果表明只从调整绿灯时间着手不结合车辆的出发组织,很难有效降低延误和压缩疏散时间。     

An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 1—Decision Support System

This article introduces a new integrated scenario-based evacuation (ISE) framework to support hurricane evacuation decision making. It explicitly captures the dynamics, uncertainty, and human–natural system interactions that are fundamental to the challenge of hurricane evacuation, but have not been fully captured in previous formal evacuation models. The hazard is represented with an ensemble of probabilistic scenarios, population behavior with a dynamic decision model, and traffic with a dynamic user equilibrium model. The components are integrated in a multistage stochastic programming model that minimizes risk and travel times to provide a tree of evacuation order recommendations and an evaluation of the risk and travel time performance for that solution. The ISE framework recommendations offer an advance in the state of the art because they: (1) are based on an integrated hazard assessment (designed to ultimately include inland flooding), (2) explicitly balance the sometimes competing objectives of minimizing risk and minimizing travel time, (3) offer a well-hedged solution that is robust under the range of ways the hurricane might evolve, and (4) leverage the substantial value of increasing information (or decreasing degree of uncertainty) over the course of a hurricane event. A case study for Hurricane Isabel (2003) in eastern North Carolina is presented to demonstrate how the framework is applied, the type of results it can provide, and how it compares to available methods of a single scenario deterministic analysis and a two-stage stochastic program.     

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.     

Estimating Evacuation Shelter Deficits in the Houston–Galveston Metropolitan Area

Evacuation is frequently used by emergency managers and other officials as part of an overall approach to reducing the morbidity and mortality associated with hurricane landfall. In this study, the evacuation shelter capacity of the Houston–Galveston Metropolitan Statistical Area (MSA) was spatially assessed and shelter deficits in the region were estimated. These data provide essential information needed to eliminate shelter deficits and ensure a successful evacuation from a future storm. Spatial statistical methods—Global Moran's I, Anselin Local Moran's I (Local Indicators of Spatial Association [LISA]), and Hot Spot Analysis (Getis-Ord Gi*) were used to assess for regional spatial autocorrelation and clustering of evacuation shelters in the Houston–Galveston MSA. Shelter deficits were estimated in four ways—the aggregate deficit for the Houston–Galveston MSA, by evacuation Zip-Zone, by county, and by distance or radii of evacuation Zip-Zone. Evacuation shelters were disproportionately distributed in the region, with lower capacity shelters clustered closer to evacuation Zip-Zones (50 miles from the Coastal Zip-Zone), and higher capacity shelters clustered farther away from the zones (120 miles from the Coastal Zip-Zone). The aggregate shelter deficit for the Houston–Galveston MSA was 353,713 persons. To reduce morbidity and mortality associated with future hurricanes in the Houston–Galveston MSA, authorities should consider the development and implementation of policies that would improve the evacuation shelter capacity of the region. Eliminating shelter deficits, which has been done successfully in the state of Florida, is an essential element of protecting the public from hurricane impacts.     

Return Migration and Decontamination After the 2011 Fukushima Nuclear Power Plant Disaster

Return migration is key to community recovery from many disasters. Japanese governments have conducted radiation decontamination efforts in the Exclusion Zone designated after the 2011 Fukushima nuclear disaster in order to encourage this outcome. Little is known, however, about the factors that influence post-disaster migrants to return, and, if people are relatively unresponsive to decontamination, then the costs of promoting recovery may exceed the benefits. We exploit a unique survey of Fukushima evacuees to determine the factors that influence their decision to return after a disaster. Location-specific capital characteristics, such as housing tenure and the extent of property damage, are estimated to be strong factors. The radiation dose rate of the home location is found to be a statistically significant factor for intent to return, but its effect is small. We also found that households with various other characteristics were noncommittal about the return option and likely to defer their decisions, which implies that “return” and “not-return” are asymmetric. Our simulation analysis found that the number of returnees encouraged by this decontamination was 12,882, less than 8% of the total evacuees, while the decontamination cost per returnee was 3.36 million USD. This result implies that the government could have improved the well-being of evacuees at a lower cost by policies other than decontamination.