Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities. The evolving risk landscape will force a departure from traditional risk assessment approaches that rely on historical data to quantify insurable risks. This article investigates the risk structure of SAVs and employs a telematics‐based anomaly detection model to assess split risk profiles. An unsupervised multivariate Gaussian (MVG) based anomaly detection method is used to identify abnormal driving patterns based on accelerometer and GPS sensors of manually driven vehicles. Parameters are inferred for vehicles equipped with semiautonomous capabilities and the resulting split risk profile is determined. The MVG approach allows for the quantification of vehicle risks by the relative frequency and severity of observed anomalies and a location‐based risk analysis is performed for a more comprehensive assessment. This approach contributes to the challenge of quantifying SAV risks and the methods employed here can be applied to evolving data sources pertinent to SAVs. Utilizing the vast amounts of sensor‐generated data will enable insurers to proactively reassess the collective performances of both the artificial driving agent and human driver.     

An Integrated Risk Assessment Model of Township‐Scaled Land Subsidence Based on an Evidential Reasoning Algorithm and Fuzzy Set Theory

Land subsidence risk assessment (LSRA) is a multi‐attribute decision analysis (MADA) problem and is often characterized by both quantitative and qualitative attributes with various types of uncertainty. Therefore, the problem needs to be modeled and analyzed using methods that can handle uncertainty. In this article, we propose an integrated assessment model based on the evidential reasoning (ER) algorithm and fuzzy set theory. The assessment model is structured as a hierarchical framework that regards land subsidence risk as a composite of two key factors: hazard and vulnerability. These factors can be described by a set of basic indicators defined by assessment grades with attributes for transforming both numerical data and subjective judgments into a belief structure. The factor‐level attributes of hazard and vulnerability are combined using the ER algorithm, which is based on the information from a belief structure calculated by the Dempster‐Shafer (D‐S) theory, and a distributed fuzzy belief structure calculated by fuzzy set theory. The results from the combined algorithms yield distributed assessment grade matrices. The application of the model to the Xixi‐Chengnan area, China, illustrates its usefulness and validity for LSRA. The model utilizes a combination of all types of evidence, including all assessment information—quantitative or qualitative, complete or incomplete, and precise or imprecise—to provide assessment grades that define risk assessment on the basis of hazard and vulnerability. The results will enable risk managers to apply different risk prevention measures and mitigation planning based on the calculated risk states.     

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness,Planning, and Response for Severe Nuclear Power Plant Accidents

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.     

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.     

Insights into the Societal Risk of Nuclear Power Plant Accidents

The elements of societal risk from a nuclear power plant accident are clearly illustrated by the Fukushima accident: land contamination, long‐term relocation of large numbers of people, loss of productive farm area, loss of industrial production, and significant loss of electric capacity. NUREG‐1150 and other studies have provided compelling evidence that the individual health risk of nuclear power plant accidents is effectively negligible relative to other comparable risks, even for people living in close proximity to a plant. The objective of this study is to compare the societal risk of nuclear power plant accidents to that of other events to which the public is exposed. We have characterized the monetized societal risk in the United States from major societally disruptive events, such as hurricanes, in the form of a complementary cumulative distribution function. These risks are compared with nuclear power plant risks, based on NUREG‐1150 analyses and new MACCS code calculations to account for differences in source terms determined in the more recent SOARCA study. A candidate quantitative societal objective is discussed for potential adoption by the NRC. The results are also interpreted with regard to the acceptability of nuclear power as a major source of future energy supply.     

Context‐Specific,Scenario‐Based Risk Scales

Reacting to an emergency requires quick decisions under stressful and dynamic conditions. To react effectively, responders need to know the right actions to take given the risks posed by the emergency. While existing research on risk scales focuses primarily on decision making in static environments with known risks, these scales may be inappropriate for conditions where the decision maker's time and mental resources are limited and may be infeasible if the actual risk probabilities are unknown. In this article, we propose a method to develop context‐specific, scenario‐based risk scales designed for emergency response training. Emergency scenarios are used as scale points, reducing our dependence on known probabilities; these are drawn from the targeted emergency context, reducing the mental resources required to interpret the scale. The scale is developed by asking trainers/trainees to rank order a range of risk scenarios and then aggregating these orderings using a Kemeny ranking. We propose measures to assess this aggregated scale's internal consistency, reliability, and validity, and we discuss how to use the scale effectively. We demonstrate our process by developing a risk scale for subsurface coal mine emergencies and test the reliability of the scale by repeating the process, with some methodological variations, several months later.     

Workers’ Perception of Chemical Risks: A Focus Group Study

Workers’ perceptions with respect to health and safety at work are rarely taken into account when considering the development of prevention programs. The aim of this study was to explore workers’ perceptions of chemical risks at the workplace, in order to investigate the prerequisites for a workplace health program. A qualitative study was conducted involving seven focus groups of 5–10 participants (blue‐collar workers) each. All groups were homogeneous in terms of sex, work status, language, and company membership. Results showed that several factors have an important influence on workers’ perception of chemical risks. Workers assess risks by means of both sensory and empirical diagnosis and are concerned about the long‐term health consequences. They perceive the threat of chemical risks as high. Despite this, they are resigned to accepting the risks. Existing formal sources of information are rarely consulted because they are judged to be difficult to understand and not user friendly. Instead, workers tend to obtain information from informal sources. Communication problems with and lack of trust in prevention advisers and hierarchy are frequently mentioned. Workers feel that their specific knowledge of their working conditions and their proposals for practical, cost‐effective solutions to improve health and safety at the workplace are insufficiently taken into account. The use of focus groups yielded a useful insight into workers’ perceptions of chemical risks. Our findings suggest that training programs for prevention advisers should include topics such as understanding of workers’ perceptions, usefulness of a participatory approach, and communication and education skills.     

In Search of Perfect Foresight? Policy Bias,Management of Unknowns,and What Has Changed in Science Policy Since the Tohoku Disaster

The failure to foresee the catastrophic earthquakes, tsunamis, and nuclear accident of 2011 has been perceived by many in Japan as a fundamental shortcoming of modern disaster risk science. Hampered by a variety of cognitive and institutional biases, the conventional disaster risk management planning based on the “known risks” led to the cascading failures of the interlinked disaster risk management (DRM) apparatus. This realization led to a major rethinking in the use of science for policy and the incorporations of lessons learned in the country's new DRM policy. This study reviews publicly available documents on expert committee discussions and scientific articles to identify what continuities and changes have been made in the use of scientific knowledge in Japanese risk management. In general, the prior influence of cognitive bias (e.g., overreliance on documented hazard risks) has been largely recognized, and increased attention is now being paid to the incorporation of less documented but known risks. This has led to upward adjustments in estimated damages from future risks and recognition of the need for further strengthening of DRM policy. At the same time, there remains significant continuity in the way scientific knowledge is perceived to provide sufficient and justifiable grounds for the development and implementation of DRM policy. The emphasis on “evidence‐based policy” in earthquake and tsunami risk reduction measures continues, despite the critical reflections of a group of scientists who advocate for a major rethinking of the country's science‐policy institution respecting the limitations of the current state science.     

Regional Risk Assessment for Point Source Pollution Based on a Water Quality Model of the Taipu River,China

Point source pollution is one of the main threats to regional environmental health. Based on a water quality model, a methodology to assess the regional risk of point source pollution is proposed. The assessment procedure includes five parts: (1) identifying risk source units and estimating source emissions using Monte Carlo algorithms; (2) observing hydrological and water quality data of the assessed area, and evaluating the selected water quality model; (3) screening out the assessment endpoints and analyzing receptor vulnerability with the Choquet fuzzy integral algorithm; (4) using the water quality model introduced in the second step to predict pollutant concentrations for various source emission scenarios and analyzing hazards of risk sources; and finally, (5) using the source hazard values and receptor vulnerability scores to estimate overall regional risk. The proposed method, based on the Water Quality Analysis Simulation Program (WASP), was applied in the region of the Taipu River, which is in the Taihu Basin, China. Results of source hazard and receptor vulnerability analysis allowed us to describe aquatic ecological, human health, and socioeconomic risks individually, and also integrated risks in the Taipu region, from a series of risk curves. Risk contributions of sources to receptors were ranked, and the spatial distribution of risk levels was presented. By changing the input conditions, we were able to estimate risks for a range of scenarios. Thus, the proposed procedure may also be used by decisionmakers for long‐term dynamic risk prediction.     

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.     

Consumption of Fish and Shrimp from Southeast Louisiana Poses No Unacceptable Lifetime Cancer Risks Attributable to High‐Priority Polycyclic Aromatic Hydrocarbons

Following oil spills such as the Deepwater Horizon accident (DWH), contamination of seafood resources and possible increased health risks attributable to consumption of seafood in spill areas are major concerns. In this study, locally harvested finfish and shrimp were collected from research participants in southeast Louisiana and analyzed for polycyclic aromatic hydrocarbons (PAHs). PAHs are some of the most important chemicals of concern regarding oil‐spill‐contaminated seafood resources during and following oil spills. Some PAHs are considered carcinogens for risk assessment purposes, and currently, seven of these can be combined in lifetime cancer risk assessments using EPA approaches. Most PAHs were not detected in these samples (minimum detection limits ranged from 1.2 to 2.1 PPB) and of those that were detected, they were generally below 10 PPB. The pattern of detected PAHs suggested that the source of these chemicals in these seafood samples was not a result of direct contact with crude oil. Lifetime cancer risks were assessed using conservative assumptions and models in a probabilistic framework for the seven carcinogenic PAHs. Lifetime health risks modeled using this framework did not exceed a 1/10,000 cancer risk threshold. Conservative, health‐protective deterministic estimates of the levels of concern for PAH chemical concentration and seafood intake rates were above the concentrations and intake rates modeled under this probabilistic framework. Taken together, consumption of finfish and shrimp harvested from southeast Louisiana following the DWH does not pose unacceptable lifetime cancer risks from these seven carcinogenic PAHs even for the heaviest possible consumers.     

More Than Fear Induction: Toward an Understanding of People's Motivation to Be Well‐Prepared for Emergencies in Flood‐Prone Areas

This article examines the extent and manner to which evaluations of flood‐related precautions are affected by an individual's motivation and perception of context. It argues that the relationship between risk perception and flood risk preparedness can be fruitfully specified in terms of vulnerability and efficacy if these concepts are put into the perspective of prevention‐focused motivation. This relationship was empirically examined in a risk communication experiment in a delta area of the Netherlands (n = 1,887). Prevention‐focused motivation was induced by contextualized risk information. The results showed that prevention‐focused individuals were more sensitive to the relevance of potential precautions for satisfying their needs in the context they found themselves in. The needs included, but were not limited to, fear reduction. Due to the heterogeneity of the residents, the evaluations reflected individual differences in the intensity and the selectivity of precautionary processes. Four types of persons could be distinguished according to their evaluation of precautionary measures: a high‐scoring minority, two more selective types, and a low‐scoring minority. For policymakers and risk communicators it is vital to consider the nature of prevention motivation and the context in which it is likely to be high.     

Risk assessment of major hazards and its application in urban planning: a case study

With the rapid development of industry in China, the number of establishments that are proposed or under construction is increasing year by year, and many are industries that handle flammable, explosive, toxic, harmful, and dangerous substances. Accidents such as fire, explosion, and toxic diffusion inevitably happen. Accidents resulting from these major hazards in cities cause a large number of casualties and property losses. It is increasingly important to analyze the risk of major hazards in cities realistically and to suitably plan and utilize the surrounding land based on the risk analysis results, thereby reducing the hazards. A theoretical system for risk assessment of major hazards in cities is proposed in this article, and the major hazard risk for the entire city is analyzed quantitatively. Risks of various major accidents are considered together, superposition effect is analyzed, individual risk contours of the entire city are drawn out, and the level of risk in the city is assessed using "as low as reasonably practicable" guidelines. After the entire city's individual risk distribution is obtained, risk zones are divided according to corresponding individual risk value of HSE, and land-use planning suggestions are proposed. Finally, a city in China is used as an example to illustrate the risk assessment process of the city's major hazard and its application in urban land-use planning. The proposed method has a certain theoretical and practical significance in establishing and improving risk analysis of major hazard and urban land-use planning. On the one hand, major urban public risk is avoided; further, the land is utilized in the best possible way in order to obtain the maximum benefit from its use.     

A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

In the United Kingdom, dwelling fires are responsible for the majority of all fire‐related fatalities. The development of these incidents involves the interaction of a multitude of variables that combine in many different ways. Consequently, assessment of dwelling fire risk can be complex, which often results in ambiguity during fire safety planning and decision making. In this article, a three‐part Bayesian network model is proposed to study dwelling fires from ignition through to extinguishment in order to improve confidence in dwelling fire safety assessment. The model incorporates both hard and soft data, delivering posterior probabilities for selected outcomes. Case studies demonstrate how the model functions and provide evidence of its use for planning and accident investigation.     

Regulating Chemicals—From Risks to Riskiness

Under current European Union legislation, action to restrict the production and use of a chemical is only justified if there is evidence that the chemical poses a risk to human health or the environment. Risk is understood as being a matter of the magnitude and probability of specifiable harms. An examination of how risks from chemicals are assessed shows the process to be fraught with uncertainty, with the result that evidence that commands agreement as to whether a chemical poses a risk or not is often not available. Hence the frequent disputes as to whether restrictions on chemicals are justified. Rather than trying to assess the risks from a chemical, I suggest that we should aim to assess how risky a chemical is in a more everyday sense, where riskiness is a matter of the possibility of harm. Risky chemicals are those where, given our state of knowledge, it is possible that they cause harm. I discuss four things that make a chemical more risky: (1) its capacity to cause harm; (2) its novelty; (3) its persistence; and (4) its mobility. Regulation of chemicals should aim to reduce the production and use of risky chemicals by requiring that the least risky substance or method is always used for any particular purpose. Any use of risky substances should be justifiable in terms of the public benefits of that use.     

Capacity Planning with Financial and Operational Hedging in Low‐Cost Countries

The authors of this article outline a capacity planning problem in which a risk‐averse firm reserves capacities with potential suppliers that are located in multiple low‐cost countries. While demand is uncertain, the firm also faces multi‐country foreign currency exposures. This study develops a mean‐variance model that maximizes the firm's optimal utility and derives optimal utility and optimal decisions in capacity and financial hedging size. The authors show that when demand and exchange rate risks are perfectly correlated, a risk‐averse firm, by using financial hedging, will achieve the same optimal utility as a risk‐neutral firm. In this study as well, a special case is examined regarding two suppliers in China and Vietnam. The results show that if a single supplier is contracted, financial hedging most benefits the highly risk‐averse firm when the demand and exchange rate are highly negatively related. When only one hedge is used, financial hedging dominates operational hedging only when the firm is very risk averse and the correlation between the two exchange rates have become positive. With both theoretical and numerical results, this study concludes that the two hedges are strategic tools and interact each other to maximize the optimal utility.     

Values and Risk Perceptions: A Cross‐Cultural Examination

This article examines the relationship between values and risk perceptions regarding terror attacks. The participants in the study are university students from Turkey (n = 536) and Israel (n = 298). Schwartz value theory (1992, 1994) is applied to conceptualize and measure values. Cognitive (perceived likelihood and perceived severity) and emotional (fear, helplessness, anger, distress, insecurity, hopelessness, sadness, and anxiety) responses about the potential of (i) being personally exposed to a terror attack, and (ii) a terror attack that may occur in one's country are assessed to measure risk perceptions. Comparison of the two groups suggests that the Turkish participants are significantly more emotional about terror risks than the Israeli respondents. Both groups perceive the risk of a terror attack that may occur in their country more likely than the risk of being personally exposed to a terror attack. No significant differences are found in emotional representations and perceived severity ratings regarding these risks. Results provide support for the existence of a link between values and risk perceptions of terror attacks. In both countries, self‐direction values are negatively related to emotional representations, whereas security values are positively correlated with emotions; hedonism and stimulation values are negatively related to perceived likelihood. Current findings are discussed in relation to previous results, theoretical approaches (the social amplification of risk framework and cultural theory of risk), and practical implications (increasing community support for a course of action, training programs for risk communicators).     

Improving Flood Risk Communication by Focusing on Prevention‐Focused Motivation

This article proposes an approach to flood risk communication that gives particular emphasis to the distinction between prevention and promotion motivation. According to E. Tory Higgins, the promotion system and the prevention system are assumed to coexist in every person, but one or the other may be temporarily or chronically more accessible. These insights have far‐reaching implications for our understanding of people's reasoning about risks. Flood risk communication framed in terms of prevention involves the notions of chance and harm, woven into a story about particular events that necessitate decisions to be more careful about safety issues and protect one's family and oneself from danger. The article describes how the insights worked out in practice, using a flood risk communication experiment among a sample from the general population in a highly populated river delta of the Netherlands. It had a posttest‐only control group design (n = 2,302). The results showed that risk communication had a large effect on the participants’ responses and that this effect was higher among chronic prevention‐focused people than among others. Any information that increased the fit between a prevention‐framed message and a person's chronic prevention motivation produced stronger situationally induced, prevention‐focused responses. This may significantly improve communication about risks. In contrast, the notion of water city projects, featuring waterside living, had more appeal to promotion‐focused people.     

The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities

Supply chain disruptions and the associated operational and financial risks represent the most pressing concern facing firms that compete in today's global marketplace. Extant research has not only confirmed the costly nature of supply chain disruptions but has also contributed relevant insights on such related issues as supply chain risks, vulnerability, resilience, and continuity. In this conceptual note, we focus on a relatively unexplored issue, asking and answering the question of how and why one supply chain disruption would be more severe than another. In doing so, we argue, de facto, that supply chain disruptions are unavoidable and, as a consequence, that all supply chains are inherently risky. Employing a multiple‐method, multiple‐source empirical research design, we derive novel insights, presented as six propositions that relate the severity of supply chain disruptions (i) to the three supply chain design characteristics of density, complexity, and node criticality and (ii) to the two supply chain mitigation capabilities of recovery and warning. These findings not only augment existing knowledge related to supply chain risk, vulnerability, resilience, and business continuity planning but also call into question the wisdom of pursuing such practices as supply base reduction, global sourcing, and sourcing from supply clusters.