Development of Risk-Based Ranking Measures of Effectiveness for the United States Coast Guard's Vessel Inspection Program

This paper describes the results of an analysis of the effectiveness of the U.S. Coast Guard's efforts to promote the safety of life and property at sea through its program to inspect large, ocean-going vessels. The effectiveness of specific U.S. Coast Guard vessel inspection activities was evaluated using risk-based metrics that linked categories of root causes of accidents to the particular inspection activities designed to reduce the risk of each root cause category. The results demonstrate a risk-based ranking of USCG inspection activities. These metrics, describes as measures of effectiveness (MOEs), yield insights regarding the most beneficial areas in which to concentrate inspection resources. Insights regarding quality of vessel casualty investigation data and database design as related to risk-based importance analysis are also discussed. The MOEs constructed in this study are specific to the USCG's Marine Inspection and Boarding Program, but the methodology of the study is based on sound theoretical principles that are probably applicable to a range of maritime safety activities. Hence the methodology applies equally to other important governmental regulatory programs and can be similarly used to measure their effectiveness and as an aid to decision-making.     

Speaking the Truth in Maritime Risk Assessment

Several major risk studies have been performed in recent years in the maritime transportation domain. These studies have had significant impact on management practices in the industry. The first, the Prince William Sound risk assessment, was reviewed by the National Research Council and found to be promising but incomplete, as the uncertainty in its results was not assessed. The difficulty in assessing this uncertainty is the different techniques that need to be used to model risk in this dynamic and data-scarce application area. In previous articles, we have developed the two pieces of methodology necessary to assess uncertainty in maritime risk assessment, a Bayesian simulation of the occurrence of situations with accident potential and a Bayesian multivariate regression analysis of the relationship between factors describing these situations and expert judgments of accident risk. In this article, we combine the methods to perform a full-scale assessment of risk and uncertainty for two case studies. The first is an assessment of the effects of proposed ferry service expansions in San Francisco Bay. The second is an assessment of risk for the Washington State Ferries, the largest ferry system in the United States.     

Epistemic Uncertainty in the Ranking and Categorization of Probabilistic Safety Assessment Model Elements: Issues and Findings

In this work, we study the effect of epistemic uncertainty in the ranking and categorization of elements of probabilistic safety assessment (PSA) models. We show that, while in a deterministic setting a PSA element belongs to a given category univocally, in the presence of epistemic uncertainty, a PSA element belongs to a given category only with a certain probability. We propose an approach to estimate these probabilities, showing that their knowledge allows to appreciate " the sensitivity of component categorizations to uncertainties in the parameter values " (U.S. NRC Regulatory Guide 1.174). We investigate the meaning and utilization of an assignment method based on the expected value of importance measures. We discuss the problem of evaluating changes in quality assurance, maintenance activities prioritization, etc. in the presence of epistemic uncertainty. We show that the inclusion of epistemic uncertainly in the evaluation makes it necessary to evaluate changes through their effect on PSA model parameters. We propose a categorization of parameters based on the Fussell-Vesely and differential importance (DIM) measures. In addition, issues in the calculation of the expected value of the joint importance measure are present when evaluating changes affecting groups of components. We illustrate that the problem can be solved using DIM. A numerical application to a case study concludes the work.     

A Risk-Based Approach to Setting Priorities in Protecting Bridges Against Terrorist Attacks

This article presents an approach to the problem of terrorism risk assessment and management by adapting the framework of the risk filtering, ranking, and management method. The assessment is conducted at two levels: (1) the system level, and (2) the asset-specific level. The system-level risk assessment attempts to identify and prioritize critical infrastructures from an inventory of system assets. The definition of critical infrastructures offered by Presidential Decision Directive 63 was used to determine the set of attributes to identify critical assets--categorized according to national, regional, and local impact. An example application is demonstrated using information from the Federal Highway Administration National Bridge Inventory for the State of Virginia. Conversely, the asset-specific risk assessment performs an in-depth analysis of the threats and vulnerabilities of a specific critical infrastructure. An illustration is presented to offer some insights in risk scenario identification and prioritization, multiobjective evaluation of management options, and extreme-event analysis for critical infrastructure protection.     

Sensitivity Analysis of Model Output with Input Constraints: A Generalized Rationale for Local Methods

In this work, we introduce a generalized rationale for local sensitivity analysis (SA) methods that allows to solve the problems connected with input constraints. Several models in use in the risk analysis field are characterized by the presence of deterministic relationships among the input parameters. However, SA issues related to the presence of constraints have been mainly dealt with in a heuristic fashion. We start with a systematic analysis of the effects of constraints. The findings can be summarized in the following three effects. (i) Constraints make it impossible to vary one parameter while keeping all others fixed. (ii) The model output becomes insensitive to a parameter if a constraint is solved for that parameter. (iii) Sensitivity analysis results depend on which parameter is selected as dependent. The explanation of these effects is found by proposing a result that leads to a natural extension of the local SA rationale introduced in Helton (1993) . We then extend the definitions of the Birnbaum, criticality, and the differential importance measures to the constrained case. In addition, a procedure is introduced that allows to obtain constrained sensitivity results at the same cost as in the absence of constraints. The application to a nonbinary event tree concludes the article, providing a numerical illustration of the above findings.     

Matrix Theory Formalism for Event Tree Analysis: Application to Nuclear-Risk Analysis

It is pointed out that the familiar analytical device of "event tree", widely used in risk analysis, can be viewed as a transition matrix expressing the likelihood of going from "entry states" to "exit states". This point of view is shown to have numerous interesting conceptual and computational features which promise to make it a very useful addition to the arsenal of tools for risk analysis. The basic idea is explained first in terms of a simple, made-up example. The application of the idea to a very real and complicated problem, nuclear-plant risk assessment, is then outlined.     

An Expected Consequence Approach to Route Choice in the Maritime Transportation of Crude Oil

Maritime transportation is the major conduit of international trade, and the primary link for global crude oil movement. Given the volume of oil transported on international maritime links, it is not surprising that oil spills of both minor and major types result, although most of the risk‐related work has been confined to the local settings. We propose an expected consequence approach for assessing oil‐spill risk from intercontinental transportation of crude oil that not only adheres to the safety guidelines specified by the International Maritime Organization but also outlines a novel technique that makes use of coarse global data to estimate accident probabilities. The proposed estimation technique, together with four of the most popular cost‐of‐spill models from the literature, were applied to study and analyze a realistic size problem instance. Numerical analyses showed that: a shorter route may not necessarily be less risky; an understanding of the inherent oil‐spill risk of different routes could potentially facilitate tanker routing decisions; and the associated negotiations over insurance premium between the transport company and the not‐for‐profit prevention and indemnity clubs. Finally, we note that only the linear model should be used with one of the three nonlinear cost‐of‐spill models for evaluating tanker routes.     

Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches

Uncertainty importance measures are quantitative tools aiming at identifying the contribution of uncertain inputs to output uncertainty. Their application ranges from food safety (Frey & Patil (2002)) to hurricane losses (Iman et al. (2005a, 2005b)). Results and indications an analyst derives depend on the method selected for the study. In this work, we investigate the assumptions at the basis of various indicator families to discuss the information they convey to the analyst/decisionmaker. We start with nonparametric techniques, and then present variance-based methods. By means of an example we show that output variance does not always reflect a decisionmaker state of knowledge of the inputs. We then examine the use of moment-independent approaches to global sensitivity analysis, i.e., techniques that look at the entire output distribution without a specific reference to its moments. Numerical results demonstrate that both moment-independent and variance-based indicators agree in identifying noninfluential parameters. However, differences in the ranking of the most relevant factors show that inputs that influence variance the most are not necessarily the ones that influence the output uncertainty distribution the most.     

Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks

This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port.     

Is It Safer to Fly or Drive?

In attempts to soothe the nascent fear of the scheduled airline traveler, passengers waiting takeoff are sometimes reminded of the cliche that they may have already completed the most dangerous part of their trip — the drive to the airport. The objective of this paper is to communicate under what conditions air travel is indeed safer than highway travel and vice versa. The conventional wisdom among risk communicators that air travel is so much safer than car travel arises from the most widely quoted death rates per billion miles for each — 0.6 for air compared to 24 for road. There are three reasons why such an unqualified comparison of aggregated fatality rates is inappropriate. First, the airline rate is passenger fatalities per passenger mile, whereas the road rate is all fatalities (any occupants, pedestrians, etc.) per vehicle mile. Second, road travel that competes with air travel is on the rural interstate system, not on average roads. Third, driver and vehicle characteristics, and driver behavior, lead to car-driver risks that vary over a wide range. Expressions derived to compare risk for drivers with given characteristics to those on airline trips of given distance showed that 40-year-old, belted, alcohol-free drivers of cars 700 pounds heavier than average are slightly less likely to be killed in 600 miles of rural interstate driving than in airline trips of the same length. Compared to this driver, 18-year-old, unbelted, intoxicated, male drivers of cars 700 pounds lighter than average have a risk over 1000 times greater. Furthermore, it is shown that the cliche above is untrue for a group of drivers having the age distribution of airline passengers.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

Quantitative Assessment of Building Fire Risk to Life Safety

This article presents a quantitative risk assessment framework for evaluating fire risk to life safety. Fire risk is divided into two parts: probability and corresponding consequence of every fire scenario. The time-dependent event tree technique is used to analyze probable fire scenarios based on the effect of fire protection systems on fire spread and smoke movement. To obtain the variation of occurrence probability with time, Markov chain is combined with a time-dependent event tree for stochastic analysis on the occurrence probability of fire scenarios. To obtain consequences of every fire scenario, some uncertainties are considered in the risk analysis process. When calculating the onset time to untenable conditions, a range of fires are designed based on different fire growth rates, after which uncertainty of onset time to untenable conditions can be characterized by probability distribution. When calculating occupant evacuation time, occupant premovement time is considered as a probability distribution. Consequences of a fire scenario can be evaluated according to probability distribution of evacuation time and onset time of untenable conditions. Then, fire risk to life safety can be evaluated based on occurrence probability and consequences of every fire scenario. To express the risk assessment method in detail, a commercial building is presented as a case study. A discussion compares the assessment result of the case study with fire statistics.     

An Approach to Assessing the Impacts of Incident-Free Transportation of Radioactive Materials: I. Air Transportation

Annual radiation doses and risks to passengers and crew are calculated for shipments of radioactive materials in passenger aircraft under accident-free and incident-free conditions (i.e., under normal transportation conditions). The 1982 database developed by Sandia National Laboratories is used, and calculations are made using RADTRAN 4.0. This paper is one of two papers estimating radiological risk associated with incident-free transportation of radioactive materials.     

An Approach to Assessing the Impacts of Incident-Free Transportation of Radioactive Materials: II. Highway Transportation

Annual radiation doses and risks to the public living near, and traveling on, public highways, and occupational doses are calculated for highway shipments of radioactive materials under accident-free and incident-free conditions (i.e., under normal transportation conditions). The database developed by Sandia National Laboratories is used, and calculations are made using RADTRAN 4.0. This report is one of two reports estimating radiological risk associated with transportation of radioactive materials.     

Influence of Safety Measures on the Risks of Transporting Dangerous Goods Through Road Tunnels

Quantitative risk assessment (QRA) models are used to estimate the risks of transporting dangerous goods and to assess the merits of introducing alternative risk reduction measures for different transportation scenarios and assumptions. A comprehensive QRA model recently was developed in Europe for application to road tunnels. This model can assess the merits of a limited number of "native safety measures." In this article, we introduce a procedure for extending its scope to include the treatment of a number of important "nonnative safety measures" of interest to tunnel operators and decisionmakers. Nonnative safety measures were not included in the original model specification. The suggested procedure makes use of expert judgment and Monte Carlo simulation methods to model uncertainty in the revised risk estimates. The results of a case study application are presented that involve the risks of transporting a given volume of flammable liquid through a 10-km road tunnel.     

Comparison of Standard Radiological Risk Models and Using RESRAD to Derive Generic Risk-Based Area Factors for Final Status Surveys

The RESidual RADioactivity (RESRAD) computer code has been used for years to calculate carcinogenic risk and radiological dose from exposure to radionuclides. The basic ingestion, inhalation, and direct gamma intake equations used by RESRAD, Risk Assessment Guidance for Superfund (RAGS), and the Soil Screening Guidance for Radionuclides (SSG) are similar and can produce similar results, but there are some notable differences. Of particular interest is the fact that RESRAD incorporates sophisticated environmental transport models. Associated environmental parameters allow risk assessors to consider, among other variables, the size (i.e., surface area) of the contaminated zone, a variable not typically addressed quantitatively under the RAGS/SSG paradigm. Considering the similarities between basic RESRAD, RAGS, and SSG intake equations and given the broad acceptance of RESRAD, it stands to reason that RESRAD-derived area factors may be used to supplement RAGS/SSG human health risk calculations. This would allow risk assessors to retrofit existing results or otherwise modify standard RAGS/SSG equations for use in site closeout planning under the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM), given a key component of the MARSSIM method is the consideration of small areas of elevated activity or "hot spots" through the use of area factors.     

The Dynamics of Risk Perception: How Does Perceived Risk Respond to Risk Events?

This paper examines the relationship between perceived risk and experience. This research addresses the processes by which people learn about risk and choose among real life prospects with associated uncertainties, risks and benefits. By comparing the impact of acute risk events with that of chronic risk events on public perception of risk during and after the events, this research focuses on the learning processes that characterize what kinds of risk events alter the perception of risk. Comparing materialized hazards at existing facilities with the risks associated with potential facilities, this research addresses risk choices among real life prospects. This study uses a classic pre-post quasi-experimental design. Surveys conducted in the Spring of 1992 on perceived and acceptable risk in Odessa and La Porte, Texas were conducted prior to risk events. Respondents from that survey were re-interviewed in the Spring of 1993 after the risk events to form a panel design. This paper analyzes the affect of risk events on perceived risk and the implications of these experiences for public policy concerning technological risk. The empirical results suggest that the social processes that construct and maintain risk in the public eye are at least as important as, if not more important than, the physical and psychological dimensions of risk.     

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health‐Care Settings

In spite of increased attention to quality and efforts to provide safe medical care, adverse events (AEs) are still frequent in clinical practice. Reports from various sources indicate that a substantial number of hospitalized patients suffer treatment‐caused injuries while in the hospital. While risk cannot be entirely eliminated from health‐care activities, an important goal is to develop effective and durable mitigation strategies to render the system “safer.” In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the health‐care domain, this can be extremely challenging due to the wide variability in the way that health‐care processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study, we have developed a generic methodology for evaluating dynamic changes in AE risk in acute care hospitals as a function of organizational and nonorganizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational‐level and policy‐level contributions to risk evolve over time, and how policies and decisions may affect the general system‐level contribution to AE risk. It also captures the feedback of organizational factors and decisions over time and the nonlinearities in these feedback effects. SD is a popular approach to understanding the behavior of complex social and economic systems. It is a simulation‐based, differential equation modeling tool that is widely used in situations where the formal model is complex and an analytical solution is very difficult to obtain. Second, a Bayesian belief network (BBN) framework is used to represent patient‐level factors and also physician‐level decisions and factors in the management of an individual patient, which contribute to the risk of hospital‐acquired AE. BBNs are networks of probabilities that can capture probabilistic relations between variables and contain historical information about their relationship, and are powerful tools for modeling causes and effects in many domains. The model is intended to support hospital decisions with regard to staffing, length of stay, and investments in safety, which evolve dynamically over time. The methodology has been applied in modeling the two types of common AEs: pressure ulcers and vascular‐catheter‐associated infection, and the models have been validated with eight years of clinical data and use of expert opinion.     

Using Choice Experiments to Assess People''s Preferences for Railway Transports of Hazardous Materials

This article investigates whether the choice experiment approach can be used to assess people's preferences and the determinants of these preferences in order to estimate the costs and benefits of different configurations of the transport of hazardous materials by rail. Changes in the exposure to hazardous materials that people are subjected to are used rather than changes in accident risk. To the best knowledge of the author, this has not been done before in a study of people's preferences toward hazardous materials. A mail survey, carried out in two cities in Sweden, is used to obtain tentative estimates of the willingness to pay for a reduction in exposure as well as the willingness to accept an increase in exposure. Special attention is given to viability, since the complexity of the activity studied, transport of hazardous materials, and the method used pose particular challenges. The response rate and tests of validity and consistency indicate that this method can be applied. Moreover, the results suggest that studies of this kind may provide guidance on changes in the transport of hazardous materials, especially because policymakers may influence the attributes presented here. Referring to the exposure of hazardous materials highlights the importance of providing the respondents with adequate information regarding hazardous transports. An important finding is that the amount of background information may have some effect on the stated preferences.