Determinants of full and partial household evacuation decision making in hurricane matthew

This paper adds partial household evacuation to the traditional binary evacuate/stay decision. Based on data from a survey of Jacksonville, FL residents after Hurricane Matthew, multinomial (MNL) and random parameter MNL models were developed to determine the influential factors and whether some variables’ effects are more nuanced than prior literature suggests. The random parameter model was preferred to the fixed parameters model. Variables significant in this model included injury concern, certainty about hurricane impact location, age, marital status, family cohesion, and living in mobile or detached homes. Greater injury concern results in lower likelihood of none of the household evacuating and greater likelihood of partial evacuation, but lower likelihood of full household evacuation. Similarly, greater certainty about hurricane impact increased the probability of partial household evacuation but decreased the probability of full evacuation. Respondent age had heterogenous effects; for 85.54% of respondents, additional years of age increased the likelihood of the household staying. Married households had a higher likelihood of staying or evacuating together. Similarly, greater family cohesion was associated with the household remaining together. Living in mobile homes decreased the likelihood that all of the household stays or evacuates and increased the probability of partial household evacuation. Living in a single-family detached home was associated with lower likelihood of all of the household staying or evacuating and a greater likelihood of a partial household evacuation. These findings can inform strategies that influence full or partial household evacuations, material requirements based on these decisions, and ways to reduce family risk.     

Unraveling the evacuation behavior of the medically fragile population: Findings from hurricane Irene

Despite the widely recognized importance of evacuation planning for residents with special needs – in this paper referred to as the medically fragile population – there is virtually no research available to guide such planning, as opposed to the numerous empirical research studies on the evacuation behavior of the general population. In this paper, we provide these long-overdue insights using data from a large-scale phone survey (over 7000 samples) conducted in the aftermath of hurricane Irene in the Hampton Roads region in Virginia. Via aggregate and disaggregate analyses, we start to unravel the behavior of this heavily understudied, and potentially vulnerable population group. Special emphasis will be placed on the differences between the medically fragile and non-medically fragile population. Two alternative definitions for what constitutes medically fragile are examined in this paper. Using the broader definition, it was found that a key difference between these two groups relates to the importance of having a strong network of family members in the area. When considering a more narrow definition, we found that being a single parent household, likelihood of neighborhood flooding and knowing most of the names of one’s neighbors have significantly different impacts on the two population groups.     

＂马太效应＂对科技奖励的影响

科技奖励中的＂马太效应＂具有双重作用,是现代科学技术发展过程中难以避免的客观现象,它既有促进科技进步,形成优势积累等积极作用,又有加剧科学共同体等级分化、阻碍科技新人发现成长及催生学术腐败等消极影响。本文系统分析了科技奖励中的＂马太效应＂的积极作用与消极影响,并提出了相应对策,从而为采取措施,趋利避害,促进科学技术稳定发展提供理论基础。     

Crying wolf: Repeat responses to hurricane evacuation orders

This article examines the evacuation behavior of residents in two South Carolina communities, Hilton Head and Myrtle Beach, during the 1996 hurricane season. Two hurricanes that approached South Carolina but hit in North Carolina allowed us to study the impact of repeated “false alarms”; (evacuations ordered based on expectations of a hurricane landfall that proved to be wrong). Differences in evacuation behavior, specific information and concerns prompting evacuation, and the reliability of information sources between hurricane events are examined to determine the impact of false alarms on the credibility of warning systems. Data were derived from a face‐to‐face survey of residents 2 weeks after Hurricane Fran in September 1996. We found that the role of official advisories was more limited than reported in previous research as people sought information on more diverse sets of concerns in their decision making. Reliance on the media and the Weather Channel, in particular, for storm characteristics and advisories was an important factor in evacuation decision making during both hurricane events. The perceived lack of reliability of gubernatorial warnings coupled with dependence on the media suggests that residents find other sources of information more personally relevant. Thus, while residents do not find that officials are “crying wolf,”; they are searching elsewhere for information to assess their own risk—what does it mean to me if there is a wolf? This increased attention toward individual differences in perceived threat may become more pronounced in future evacuations from hurricanes.     

“卡特里娜”飓风对上海市海塘管理的启示

该文收集整编了“卡特里娜”飓风袭击美国新奥尔良市之后新奥尔良市受灾情况资料,简述了该飓风重创美国的原因和美国自身的深刻反思。通过对上海市与新奥尔良市的社会经济等情况比较,从台风发生频率、潮位、地面沉降、防灾能力、日常管理、防汛手段等方面分析了上海市海塘防汛的近忧和远虑。详细阐明了“卡特里娜”飓风对上海市海塘管理的启示,以指导今后防汛防台工作。     

Hurricane evacuation demand models with a focus on use for prediction in future events

Although substantial literature exists on understanding hurricane evacuation behavior, few studies have developed models that can be used for predicting evacuation rates in future events. For this paper, we develop new ordered probit models for evacuation using survey data collected in the hurricane-prone state of North Carolina in 2011 and 2012. Since all covariates in the models are available from the census or based on location, the new models can be applied to predict evacuation rates for any future hurricane. The out-of-sample predictive power of the new models are evaluated at the individual household level using cross validation, and the aggregated level using available data from Hurricane Irene (2011), Hurricane Isabel (2003) and Hurricane Floyd (1999). Model results are also compared with an existing participation rate model, and a logistic regression model available from the literature. Results at the individual household level suggests approximately 70% of households’ evacuation behavior will be predicted correctly. Errors are evenly divided between false positives and false negatives, and with accuracy increasing to 100% as the percentage of people who actually evacuate goes to zero or all and decreasing to about 50% when the population is divided and about half of all households actually evacuate. Aggregate results suggest the new models compare favorably to the available ones, with average aggregate evacuation rate errors of five percentage points.     

Analysis of hurricane evacuee mode choice behavior

The purpose of this study is to explain the evacuee mode choice behavior of Miami Beach residents using survey data from a hypothetical category four hurricane to reveal different evacuees’ plans. Evacuation logistics should incorporate the needs of transit users and car-less populations with special attention and proper treatment. A nested logit model has been developed to explain the mode choice decisions for evacuees’ from Miami Beach who use non-household transportation modes, such as special evacuation bus, taxi, regular bus, riding with someone from another household and another type of mode denoted and aggregated as other. Specifically, the model explains that the mode choice decisions of evacuees’, who are likely to use different non-household transportation modes, are influenced by several determining factors related to evacuees’ socio-demographics, household characteristics, evacuation destination and previous experience. The findings of this study will help emergency planners and policy-makers to develop better evacuation plans and strategies for evacuees depending on others for their evacuation transportation.     

Estimating road network accessibility during a hurricane evacuation: A case study of hurricane Irma in Florida

Understanding the spatio-temporal road network accessibility during a hurricane evacuation—the level of ease of residents in an area in reaching evacuation destination sites through the road network—is a critical component of emergency management. While many studies have attempted to measure road accessibility (either in the scope of evacuation or beyond), few have considered both dynamic evacuation demand and characteristics of a hurricane. This study proposes a methodological framework to achieve this goal. In an interval of every six hours, the method first estimates the evacuation demand in terms of number of vehicles per household in each county subdivision (sub-county) by considering the hurricane’s wind radius and track. The closest facility analysis is then employed to model evacuees’ route choices towards the predefined evacuation destinations. The potential crowdedness index (PCI), a metric capturing the level of crowdedness of each road segment, is then computed by coupling the estimated evacuation demand and route choices. Finally, the road accessibility of each sub-county is measured by calculating the reciprocal of the sum of PCI values of corresponding roads connecting evacuees from the sub-county to the designated destinations. The method is applied to the entire state of Florida during Hurricane Irma in September 2017. Results show that I-75 and I-95 northbound have a high level of congestion, and sub-counties along the northbound I-95 suffer from the worst road accessibility. In addition, this research performs a sensitivity analysis for examining the impacts of different choices of behavioral response curves on accessibility results.     

港口国监督检查选船如何避免产生“马太效应”

文中从概念、历史数据、数学模型角度分析了当前港口国监督检查中的目标因素评价体系，以及其存在阶段性正反馈所导致马太效应的原因，认为这种评价体系由于缺乏科学的动态正反馈限制以及连续性计算，而将不可避免地导致具有不良记录的船舶在一段时间内无法公正地获得与其他船舶一样的对待。笔者借此文介绍了各备忘录组织所采用的“黑名单-灰名单-白名单”船旗国分类方法，并从数学角度对其进行了分析，认为其能够较好地解决正反馈限制修正以及连续性计算问题，建议仿效其建立“黑名单-灰名单-白名单”船舶分类方法，通过登轮系数修正目标因素值作为选船的依据之一，从而给具有不良记录的船舶以彻底改善船况、重新获得信用的机会。     

Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory

This paper analyzes the observed decision-making behavior of a sample of individuals impacted by Hurricane Irma in 2017 (n = 645) by applying advanced methods based in discrete choice theory. Our first contribution is identifying population segments with distinct behavior by constructing a latent class choice model for the choice whether to evacuate or not. We find two latent segments distinguished by demographics and risk perception that tend to be either evacuation-keen or evacuation-reluctant and respond differently to mandatory evacuation orders.Evacuees subsequently face a multi-dimensional choice composed of concurrent decisions of their departure day, departure time of day, destination, shelter type, transportation mode, and route. While these concurrent decisions are often analyzed in isolation, our second contribution is the development of a portfolio choice model (PCM), which captures decision-dimensional dependency (if present) without requiring choices to be correlated or sequential. A PCM reframes the choice set as a bundle of concurrent decision dimensions, allowing for flexible and simple parameter estimation. Estimated models reveal subtle yet intuitive relations, creating new policy implications based on dimensional variables, secondary interactions, demographics, and risk-perception variables. For example, we find joint preferences for early-nighttime evacuations (i.e., evacuations more than three days before landfall and between 6:00 pm and 5:59 am) and early-highway evacuations (i.e., evacuations more than three days before landfall and on a route composed of at least 50% highways). These results indicate that transportation agencies should have the capabilities and resources to manage significant nighttime traffic along highways well before hurricane landfall.