Jason R. W. Merrick

A traffic density analysis of proposed ferry service expansion in San Francisco Bay using a maritime simulation model

Abstract A proposal has been made to the California legislature to dramatically increase the frequency and coverage of ferry service in the San Francisco Bay area. A major question in the approval process is the effect of this expansion on the level of congestion on the waterway and the effect this will have on the safety of vessels in the area. A simulation model was created to estimate the number of vessel interactions in the current system and their increases caused by three alternative expansion plans. The output of the simulation model is a geographic profile showing the frequency of vessel interactions across the study area, thus representing the level of congestion under each alternative. Comparing these geographic interaction profiles to a similar one generated for the current ferry service in the San Francisco Bay allows evaluation of the increase in exposure of ferries to adverse conditions, such as, for example, the interaction of high-speed ferries in restricted visibility conditions. This analysis has been submitted to the legislature as part of the overall assessment of the proposal and will be used in the expansion decision.

The Prince William Sound Risk Assessment

After the grounding of the Exxon Valdez and its subsequent oil spill, all parties with interests in Prince William Sound (PWS) were eager to prevent another major pollution event. While they implemented several measures to reduce the risk of an oil spill, the stakeholders disagreed about the effectiveness of these measures and the potential effectiveness of further proposed measures. They formed a steering committee to represent all the major stakeholders in the oil industry, in the government, in local industry, and among the local citizens. The steering committee hired a consultant team, which created a detailed model of the PWS system, integrating system simulation, data analysis, and expert judgment. The model was capable of assessing the current risk of accidents involving oil tankers operating in the PWS and of evaluating measures aimed at reducing this risk. The risk model showed that actions taken prior to the study had reduced the risk of oil spill by 75 percent, and it identified measures estimated to reduce the accident frequency by an additional 68 percent, including improving the safety-management systems of the oil companies and stationing an enhanced-capability tug, called the Gulf Service, at Hinchinbrook Entrance. In all, various stakeholders made multimillion dollar investments to reduce the risk of further oil spills based on the results of the risk assessment.

A Risk Management Procedure for the Washington State Ferries

The state of Washington operates the largest passenger vessel ferry system in the United States. In part due to the introduction of high-speed ferries, the state of Washington established an independent blue-ribbon panel to assess the adequacy of requirements for passenger and crew safety aboard the Washington state ferries. On July 9, 1998, the Blue Ribbon Panel on Washington State Ferry Safety engaged a consultant team from The George Washington University and Rensselaer Polytechnic Institute/Le Moyne College to assess the adequacy of passenger and crew safety in the Washington state ferry (WSF) system, to evaluate the level of risk present in the WSF system, and to develop recommendations for prioritized risk reduction measures, which, once implemented, can improve the level of safety in the WSF system. The probability of ferry collisions in the WSF system was assessed using a dynamic simulation methodology that extends the scope of available data with expert judgment. The potential consequences of collisions were modeled in order to determine the requirements for onboard and external emergency response procedures and equipment. The methodology was used to evaluate potential risk reduction measures and to make detailed risk management recommendations to the blue-ribbon panel and the Washington State Transportation Commission.

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.

On a risk management analysis of oil spill risk using maritime transportation system simulation

Is it safer for New Orleans river gambling boats to be underway than to be dockside? Is oil transportation risk reduced by lowering wind restrictions from 45 to 35 knots at Hinchinbrook Entrance for laden oil tankers departing Valdez, Alaska? Should the International Safety Management (ISM) code be implemented fleet-wide for the Washington State Ferries in Seattle, or does it make more sense to invest in additional life craft? Can ferry service in San Francisco Bay be expanded in a safe manner to relieve high way congestion? These risk management questions were raised in a series of projects spanning a time frame of more than 10 years. They were addressed using a risk management analysis methodology developed over these years by a consortium of universities. In this paper we shall briefly review this methodology which integrates simulation of Maritime Transportation Systems (MTS) with incident/accident data collection, expert judgment elicitation and a consequence model. We shall describe recent advances with respect to this methodology in more detail. These improvements were made in the context of a two-year oil transportation risk study conducted from 2006–2008 in the Puget Sound and surrounding waters. An application of this methodology shall be presented comparing the risk reduction effectiveness analysis of a one-way zone, an escorting and a double hull requirement in the same context.

Risk modeling in distributed, large-scale systems

Risk is inherent in distributed, large-scale systems. The paper explores the challenges of risk modeling in such systems, and suggests a risk modeling approach that is responsive to the requirements of complex, distributed, large-scale systems. An example of the use of the approach in the marine transportation system is given. The paper concludes with a discussion of limitations of the approach and of future work.

A Multiple-Objective Decision Analysis of Stakeholder Values to Identify Watershed Improvement Needs

This paper describes the use of multiple-objective decision analysis to qualitatively and quantitatively assess the quality of an endangered watershed and guide future efforts to improve the quality of the watershed. The Upham Brook Watershed is an urban watershed that lies at the interface of declining inner-city Richmond, Virginia, and growth-oriented Henrico County. A section of stream within the watershed has been identified as so dangerously polluted that it threatens the health of the residents who live within the watershed boundaries. With funding provided by the National Science Foundation, the Upham Brook Watershed project committee was formed to address the quality of the Upham Brook Watershed; it consisted of experts from multiple disciplines: stream ecology, environmental policy, water policy, ground and surface water hydrology and quality, aquatic biology, political science, sociology, citizen participation, community interaction, psychology, and decision and risk analysis. Each members values and goals were brought together using a watershed management framework to meet the overall objective of the committee: to maximize the quality of the Upham Brook Watershed. The resulting model was used to identify the largest value gaps and to identify future programs needed to improve the quality of the watershed.

A Comparative Analysis of PRA and Intelligent Adversary Methods for Counterterrorism Risk Management

In counterterrorism risk management decisions, the analyst can choose to represent terrorist decisions as defender uncertainties or as attacker decisions. We perform a comparative analysis of probabilistic risk analysis (PRA) methods including event trees, influence diagrams, Bayesian networks, decision trees, game theory, and combined methods on the same illustrative examples (container screening for radiological materials) to get insights into the significant differences in assumptions and results. A key tenent of PRA and decision analysis is the use of subjective probability to assess the likelihood of possible outcomes. For each technique, we compare the assumptions, probability assessment requirements, risk levels, and potential insights for risk managers. We find that assessing the distribution of potential attacker decisions is a complex judgment task, particularly considering the adaptation of the attacker to defender decisions. Intelligent adversary risk analysis and adversarial risk analysis are extensions of decision analysis and sequential game theory that help to decompose such judgments. These techniques explicitly show the adaptation of the attacker and the resulting shift in risk based on defender decisions.

Accident precursors and safety nets: Leading indicators of tanker operations safety

Leading indicators, one type of accident precursor, are conditions, events or measures that precede an undesirable event and that have some value in predicting the arrival of the event, whether it is an accident, incident, near miss, or undesirable safety state. Leading indicators are associated with proactive activities that identify hazards and assess, eliminate, minimize and control risk. An empirical analysis of leading indicators of safety for an international energy transportation company was undertaken, utilizing a previously validated research model. Quantitative safety performance and qualitative safety culture data were obtained from 943 participants on 37 vessels from three fleets in the organization. Organizational, vessel and individual safety factors and leading indicators were identified and an analysis of fleet, vessel, and individual safety cultures was undertaken. The results indicate that individual and vessel-level leading indicators can provide important input to an organizations continuous safety measuring and monitoring systems.

A Bayesian paired comparison approach for relative accident probability assessment with covariate information

One of the challenges managers face when trying to understand complex, technological systems (in their efforts to mitigate system risks) is the quantification of accident probability, particularly in the case of rare events. Once this risk information has been quantified, managers and decision makers can use it to develop appropriate policies, design projects, and/or allocate resources that will mitigate risk. However, rare event risk information inherently suffers from a sparseness of accident data. Therefore, expert judgment is often elicited to develop frequency data for these high-consequence rare events. When applied appropriately, expert judgment can serve as an important (and, at times, the only) source of risk information. This paper presents a Bayesian methodology for assessing relative accident probabilities and their uncertainty using paired comparison to elicit expert judgments. The approach is illustrated using expert judgment data elicited for a risk study of the largest passenger ferry system in the US.