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In 2009, two trains of Washington, DC's Metrorail system collided, resulting in nine deaths and 50 serious injuries. Based on a multiwave survey of Metrorail users in the months after the crash, this article reports how the accident appears to have (1) changed over time the tradeoffs among safety, speed, frequency of service, cost, and reliability that the transit users stated they were willing to make in the postaccident period and (2) altered transit users' concerns about safety as a function of time and distance from the accident site. We employ conditional logit models to examine tradeoffs among stated preferences for system performance measures after the accident, as well as the influence that respondent characteristics of transit use, location, income, age, and gender have on these preference tradeoffs. As expected, respondents appear averse to longer headways between trains, longer travel durations, higher travel costs, a higher number of late trains, and a higher number of fatalities. The models also show evidence of higher aversion to fatalities from transit system operation among females compared to males. In addition, respondents less experienced with Metrorail travel and those with lower household incomes show higher aversion to fatalities, and this aversion increases as a subject's psychological distance from the accident site decreases. Contrary to expectations shaped by previous studies, aversion to fatalities appears to have increased between the early months after the accident and the end of the survey period, and the expected relationship between age and aversion to fatalities is not statistically significant.

Emergency management agencies and departments of transportation benefit from transportation simulation support when developing their emergency response or evacuation plans. No-notice events are increasingly becoming part of these plans. Few, if any, studies have shown how to operationalize general no-notice evacuation considerations. To fill this gap, this article describes essential features and reasonable assumptions that should be considered in the development of no-notice evacuation scenarios for use in conjunction with transportation simulation models. Although the information presented here centers on a specific location and disaster, the concepts may be generalized and adapted for use in other locations and hazards and are of value to both practitioners as well as researchers seeking to develop similar models.

AbstractConceptualizing, assessing, and managing disaster risks involve collecting and synthesizing pluralistic information—from natural, built, and human systems—to characterize disaster impacts and guide policy on effective resilience investments. Disaster research and practice, therefore, are highly complex and inherently interdisciplinary endeavors. Characterizing the uncertainties involved in interdisciplinary disaster research is imperative, since misrepresenting uncertainty can lead to myopic decisions and suboptimal societal outcomes. Efficacious disaster mitigation should, therefore, explicitly address the uncertainties associated with all stages of hazard modeling, preparation, and response. However, uncertainty assessment and communication in the context of interdisciplinary disaster research remain understudied. In this "Perspective" article, we argue that in harnessing interdisciplinary methods and diverse data types in disaster research, careful deliberations on assessing Type III and Type IV errors are imperative. Additionally, we discuss the pathologies in frequentist approaches, calling for an increasing role for Bayesian methods in uncertainty estimations. Moreover, we discuss the potential tradeoffs associated with information and uncertainty, calling for deliberate consideration of the role of diversity of information prior to setting the scope in interdisciplinary modeling. Future research guided by further reflections on the ideas raised in this article could help push the frontiers of uncertainty estimation in interdisciplinary hazard research and practice.

Natural and technological hazards requiring evacuation management -- Protective actions and protective action decision makin -- Who leaves and who does not -- When do evacuees leave -- Managing evacuation logistics -- Evacuation behavioral forecasts -- Strategies for managing evacuation demand and capacity -- Evacuation traffic modeling and simulation -- Evacuation termination and reentry -- Case studies

AbstractIn hazard and disaster contexts, human‐centered approaches are promising for interdisciplinary research since humans and communities feature prominently in many definitions of disaster and the built environment is designed and constructed by humans to serve their needs. With a human‐centered approach, the decision‐making agent becomes a critical consideration. This article discusses and illustrates the need for alignment of decision‐making agents, time, and space for interdisciplinary research on hurricanes, particularly evacuation and the immediate aftermath. We specifically consider the fields of sociobehavioral science, transportation engineering, power systems engineering, and decision support systems in this context. These disciplines have historically adopted different decision‐making agents, ranging from individuals to households to utilities and government agencies. The fields largely converged to the local level for studies' spatial scales, with some extensions based on the physical construction and operation of some systems. Greater discrepancy across the fields is found in the frequency of data collection, which ranges from one time (e.g., surveys) to continuous monitoring systems (e.g., sensors). Resolving these differences is important for the success of interdisciplinary teams in protective‐action‐related disaster research.

During evacuations, households make a number of important, related choices including accommodation type, destination, and departure time. They may make trade-offs among these choices where one decision affects the others. The analysis models the linkages among these three aforementioned choices using data from a household behavioral intention survey conducted in 2017 in the Hampton Roads, VA area. Statistical tests and a theoretical basis show that the approach that best fits the dataset was to estimate the three choices in a sequence, where the first decision serves as an independent variable in the next choice process. To model the sequence, we began by modeling accommodation choice using a multinomial logit (MNL) model. Next, the accommodation choice decisions were used with other control variables to estimate destination choice in a second MNL model. Last, evacuation distance (related to destination decisions) was used in a Cox proportional-hazards model to estimate departure time choices. The models that provide the best estimates included the following control variables that help explain the sequence of decisions residents in the Hampton Roads area expect to make: (1) a variable expressing residential stability helps explain accommodation choice; (2) prior evacuation experience, the geographic location of a household, and the duration of living in the area help predict the destination choice; and (3) distance to the chosen destination helps predict departure time. Findings from this study provide evidence that the decisions associated with these three choices influence each other and help emergency managers identify additional actions that potentially can improve the evacuation experiences of local residents.

Abstract
Hurricanes are one of the most dangerous catastrophes faced by the USA. The associated life losses can be reduced by proper planning and estimation of evacuation demand by emergency planners. Traditional evacuation demand estimation involves a sequential process of estimating various decisions such as whether to evacuate or stay, evacuation destination, and accommodation type. The understanding of this sequence is not complete nor restricted to strict sequential ordering. For instance, it is not clear whether the evacuation destination decision is made before the accommodation type decision, or the accommodation type decision is made first or both are simultaneously made. In this paper, we develop a nested logit model to predict the relative ordering of evacuation destination and accommodation type that considers both sequential and simultaneous decision making. Household survey data from Hurricane Matthew is used for computing empirical results. Empirical results underscore the importance of developing a nested structure among various outcomes. In addition to variables related to risk perception and household characteristics, it is found that social networks also affect this decision-making process.

AbstractBuilding an interdisciplinary team is critical to disaster response research as it often deals with acute onset events, short decision horizons, constrained resources, and uncertainties related to rapidly unfolding response environments. This article examines three teaming mechanisms for interdisciplinary disaster response research, including ad hoc and/or grant proposal driven teams, research center or institute based teams, and teams oriented by matching expertise toward long‐term collaborations. Using hurricanes as the response context, it further examines several types of critical data that require interdisciplinary collaboration on collection, integration, and analysis. Last, suggesting a data‐driven approach to engaging multiple disciplines, the article advocates building interdisciplinary teams for disaster response research with a long‐term goal and an integrated research protocol.