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Two recent trends made this project possible: (1) The recognition that near misses can be predictors of future negative events and (2) enhanced artificial intelligence (AI) and machine learning (ML) tools that make data analytics accessible for many organizations. Increasingly, organizations are learning from prior incidents to improve safety and reduce accidents. The U.S. Coast Guard (USCG) uses a reporting system called the Marine Information for Safety and Law Enforcement (MISLE) database. Because many of the incidents that appear in this database are minor ones, this project initially focused on determining if near misses in MISLE could be predictors of future accidents. The analysis showed that recent near-miss counts are useful for predicting future serious casualties at the waterway level. Using this finding, a predictive AI/ML model was built for each waterway type by vessel combination. Random forest decision tree AI/ML models were used to identify waterways at significant accident risk. An R-based predictive model was designed to be run monthly, using data from prior months to make future predictions. The prediction models were trained on data from 2007 to 2022 and tested on 10 months of data from 2022, where prior months were added to test the next month. The overall accuracy of the predictions was 92%-99.9%, depending on model characteristics. The predictions of the models were considered accurate enough to be potentially useful in future prevention efforts for the USCG and may be generalizable to other industries that have near-miss data and a desire to identify and manage risks.

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In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost-saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013-2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway-Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100-car trains is associated with 1.11 (95% confidence interval: 1.10-1.12) times the derailment odds of running 50-car trains (or a 11% increase), even accounting for the fact that only half as many 100-car trains would need to run. For 200-car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20-1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding.

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Many of the most complicated and pressing problems in hazards research require the integration of numerous disciplines. The lack of a common knowledge base, however, often prohibits clear communication and interaction among interdisciplinary researchers, sometimes leading to unsuccessful outcomes. Drawing on experience with several projects and collective expertise that spans multiple disciplines, the authors argue that a promising way to enhance participation and enable communication is to have a common model, or boundary object, that can integrate knowledge from different disciplines. The result is that researchers from different disciplines who use different research methods and approaches can work together toward a shared goal. This article offers four requirements for boundary objects that may enhance hazards research. Based on these requirements, agent-based models have the necessary characteristics to be a boundary object. The article concludes by examining both the value of and the challenges from using agent-based models as the boundary object in interdisciplinary projects.

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In recent years, the U.S. commercial airline industry has achieved unprecedented levels of safety, with the statistical risk associated with U.S. commercial aviation falling to 0.003 fatalities per 100 million passengers. But decades of research on organizational learning show that success often breeds complacency and failure inspires improvement. With accidents as rare events, can the airline industry continue safety advancements? This question is complicated by the complex system in which the industry operates where chance combinations of multiple factors contribute to what are largely probabilistic (rather than deterministic) outcomes. Thus, some apparent successes are realized because of good fortune rather than good processes, and this research intends to bring attention to these events, the near-misses. The processes that create these near-misses could pose a threat if multiple contributing factors combine in adverse ways without the intervention of good fortune. Yet, near-misses (if recognized as such) can, theoretically, offer a mechanism for continuing safety improvements, above and beyond learning gleaned from observable failure. We test whether or not this learning is apparent in the airline industry. Using data from 1990 to 2007, fixed effects Poisson regressions show that airlines learn from accidents (their own and others), and from one category of near-misses-those where the possible dangers are salient. Unfortunately, airlines do not improve following near-miss incidents when the focal event has no clear warnings of significant danger. Therefore, while airlines need to and can learn from certain near-misses, we conclude with recommendations for improving airline learning from all near-misses.

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Disasters garner attention when they occur, and organizations commonly extract valuable lessons from visible failures, adopting new behaviors in response. For example, the United States saw numerous security policy changes following the September 11 terrorist attacks and emergency management and shelter policy changes following Hurricane Katrina. But what about those events that occur that fall short of disaster? Research that examines prior hazard experience shows that this experience can be a mixed blessing. Prior experience can stimulate protective measures, but sometimes prior experience can deceive people into feeling an unwarranted sense of safety. This research focuses on how people interpret near-miss experiences. We demonstrate that when near-misses are interpreted as disasters that did not occur and thus provide the perception that the system is resilient to the hazard, people illegitimately underestimate the danger of subsequent hazardous situations and make riskier decisions. On the other hand, if near-misses can be recognized and interpreted as disasters that almost happened and thus provide the perception that the system is vulnerable to the hazard, this will counter the basic "near-miss" effect and encourage mitigation. In this article, we use these distinctions between resilient and vulnerable near-misses to examine how people come to define an event as either a resilient or vulnerable near-miss, as well as how this interpretation influences their perceptions of risk and their future preparedness behavior. Our contribution is in highlighting the critical role that people's interpretation of the prior experience has on their subsequent behavior and in measuring what shapes this interpretation.

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Since the 9/11 terrorist attacks, there has been an increase in public discussion regarding U.S. domestic intelligence activities. Domestic intelligence activities focus on gathering information about potential threats from individuals within the United States, and completely rational members of the public can have different opinions about the acceptability of various alternatives depending on one's values toward privacy, civil liberty, and security. Past studies have demonstrated that construction of a multiobjective value model can help clarify public values in controversial risk debates. This research explores a range of domestic intelligence alternatives that vary on multiple objectives, and applies value-focused thinking to develop a multiattribute utility model to evaluate and compare the alternatives. The process demonstrates the feasibility of eliciting model parameters from individuals and provides a method for identifying the locus of possible disagreements among individuals. The development of the model is described first, followed by insights found from participants who provided both value tradeoffs and performance scores for six different domestic intelligence alternatives. The participants were two student groups and a group of police officers. The analysis showed differences among weights for an additive model for different stakeholder groups and differences among the performance scores. In particular, there is a "halo" effect for alternatives, such that its supporters ranked the alternative higher on all attributes compared to respondents who find the alternative unacceptable. This modeling approach and results offer organizations such as the Department of Homeland Security insights into the debate surrounding new policy initiatives, particularly those requiring sensitive value tradeoffs.

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Prior research shows that when people perceive the risk of some hazardous event to be low, they are unlikely to engage in mitigation activities for the potential hazard. We believe one factor that can lower inappropriately (from a normative perspective) people's perception of the risk of a hazard is information about prior near-miss events. A near-miss occurs when an event (such as a hurricane), which had some nontrivial probability of ending in disaster (loss of life, property damage), does not because good fortune intervenes. People appear to mistake such good fortune as an indicator of resiliency. In our first study, people with near-miss information were less likely to purchase flood insurance, and this was shown for both participants from the general population and individuals with specific interests in risk and natural disasters. In our second study, we consider a different mitigation decision, that is, to evacuate from a hurricane, and vary the level of statistical probability of hurricane damage. We still found a strong effect for near-miss information. Our research thus shows how people who have experienced a similar situation but escape damage because of chance will make decisions consistent with a perception that the situation is less risky than those without the past experience. We end by discussing the implications for risk communication.

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This article describes the anti-terrorism risk-based decision aid (ARDA), a risk-based decision-making approach for prioritizing anti-terrorism measures. The ARDA model was developed as part of a larger effort to assess investments for protecting U.S. Navy assets at risk and determine whether the most effective anti-terrorism alternatives are being used to reduce the risk to the facilities and war-fighting assets. With ARDA and some support from subject matter experts, we examine thousands of scenarios composed of 15 attack modes against 160 facility types on two installations and hundreds of portfolios of 22 mitigation alternatives. ARDA uses multiattribute utility theory to solve some of the commonly identified challenges in security risk analysis. This article describes the process and documents lessons learned from applying the ARDA model for this application.

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Some program managers share a common belief that adding a redundant component to a system reduces the probability of failure by half. This is true only if the failures of the redundant components are independent events, which is rarely the case. For example, the redundant components may be subjected to the same external loads. There is, however, in general a decrease in the failure probability of the system. Nonetheless, the redundant element comes at a cost, even if it is less than that of developing the first one when both are based on the same design. Identical parts save the most in terms of design costs, but are subjected to common failure modes from possible design errors that limit the effectiveness of the redundancy. In the development of critical systems, managers thus need to decide if the costs of a parallel system are justified by the increase in the system's reliability. NASA, for example, has used redundant spacecraft to increase the chances of mission success, which worked well in the cases of the Viking and Voyager missions. These two successes, however, do not guarantee future ones. We present here a risk analysis framework accounting for dependencies to support the decision to launch at the same time a twin mission of identical spacecraft, given incremental costs and risk-reduction benefits of the second one. We illustrate this analytical approach with the case of the Mars Exploration Rovers launched by NASA in 2003, for which we had performed this assessment in 2001.

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