Risk-Based Ranking of Dominant Contributors to Maritime Pollution Events

This report describes a conceptual approach for identifying dominant contributors to risk from maritime shipping of hazardous materials. Maritime transportation accidents are relatively common occurrences compared to more frequently analyzed contributors to public risk. Yet research on maritime safety and pollution incidents has not been guided by a systematic, risk-based approach. Maritime shipping accidents can be analyzed using event trees to group the accidents into "bins," or groups, of similar characteristics such as type of cargo, location of accident (e.g., harbor, inland waterway), type of accident (e.g., fire, collision, grounding), and size of release. The importance of specific types of events to each accident bin can be quantified. Then the overall importance of accident events to risk can be estimated by weighting the events' individual bin importance measures by the risk associated with each accident bin.     

Accidents and Unpleasant Incidents: Worry in Transport and Prediction of Travel Behavior

Worry on nine different means of transport was measured in a Norwegian sample of 853 respondents. The main aim of the study was to investigate differences in worry about accidents and worry about unpleasant incidents, and how these two sorts of worry relate to various means of transport as well as transport behavior. Factor analyses of worry about accidents suggested a division between rail transport, road transport, and nonmotorized transport, whereas analyses of worry about unpleasant incidents suggested a division between transport modes where you interact with other people and "private" transport modes. Moreover, mean ratings of worry showed that respondents worried more about accidents than unpleasant incidents on private transport modes, and more about unpleasant incidents than accidents on public transport modes. Support for the distinction between worry about accidents and unpleasant incidents was also found when investigating relationships between both types of worry and behavioral adaptations: worry about accidents was more important than worry about unpleasant incidents in relation to behavioral adaptations on private means of transport, whereas the opposite was true for public means of transport. Finally, predictors of worry were investigated. The models of worry about accidents and worry about unpleasant incidents differed as to what predictors turned out significant. Knowledge about peoples' worries on different means of transport is important with regard to understanding and influencing transport and travel behavior, as well as attending to commuters' welfare.     

Risk Analysis of the Vessel Traffic in the Strait of Istanbul

The Strait of Istanbul, the narrow waterway separating Europe from Asia, holds a strategic importance in maritime transportation as it links the Black Sea to the Mediterranean. It is considered as one of the world's most congested and difficult-to-navigate waterways. Over 55,000 transit vessels pass through the Strait annually, roughly 20% of which carry dangerous cargo. In this study, we have analyzed safety risks pertaining to transit vessel maritime traffic in the Strait of Istanbul and proposed ways to mitigate them. Safety risk analysis was performed by incorporating a probabilistic accident risk model into the simulation model. A mathematical risk model was developed based on probabilistic arguments regarding instigators, situations, accidents, consequences, and historical data, as well as subject-matter expert opinions. Scenario analysis was carried out to study the behavior of the accident risks, with respect to changes in the surrounding geographical, meteorological, and traffic conditions. Our numerical investigations suggested some significant policy indications. Local traffic density and pilotage turned out to be two main factors affecting the risks at the Strait of Istanbul. Results further indicate that scheduling changes to allow more vessels into the Strait will increase risks to extreme levels. Conversely, scheduling policy changes that are opted to reduce risks may cause major increases in average vessel waiting times. This in turn signifies that the current operations at the Strait of Istanbul have reached a critical level beyond which both risks and vessel delays are unacceptable.     

Participatory analysis of accidents and incidents as a tool for increasing safety behaviour in fishermen. A pilot intervention study

Although occupational accidents are common in fishery, safety work is often not given priority by the fishermen. The aims of this study were to test a group-based intervention for increased activity in safety work through group discussion of accident/incident experience; to study occurred incidents/accidents and how such events were managed; and to study intervention effects on activity in safety work, risk acceptance and perceived manageability of risks. A sample of men from five crews (11 men) of Swedish fishermen participated. The study had a one group pre-test-post-test design for questionnaire data. The emphasis was on qualitative information collected during the intervention and interviews. The results indicated that accident causes could be appraised as being unmanageable even when technical solutions were possible. Psychological factors may cause incidents not to be documented or discussed. Incident experience seldom leads to preventive measures. Interaction between structural, social and psychological factors seemed to explain this. Questionnaires, observations and interview data suggested that some increase in safety work took place during the intervention. After the discussions the participants perceived risks to be less manageable. The study indicated that, although sensitive to dropout, participative safety interventions in fishery are feasible and may be effective. A longer or more intensive intervention may be necessary in order to progress from problem orientation to action orientation.     

Modeling Ship Transportation Risk

This article presents results from the Commission of the European Communities (CEC) project 'Safety of Shipping in Coastal Waters' (SAFECO). The project was performed by ten European partners during the period 1995-1998. The principal aim of the SAFECO project was to determine the influences that could increase the safety of shipping in coastal waters by analyzing the underlying factors contributing to the marine accident risk level. The work reported here focuses on the Marine Accident Risk Calculation System (MARCS) that was further developed during the SAFECO project. This paper presents the methods used by MARCS, as well as data and results from a 'demonstration of concept' case study covering the North Sea area. The estimated accident frequencies (number of accidents per year) were compared with historical accident data, to demonstrate the validity of the modeling approach. Reasonable (within a factor of 5) to good (within a factor of 2) agreement between calculated accident frequencies and observed accident statistics was generally obtained. However, significant discrepancies were identified for some ship types and accident categories. The risk model has particular problems with estimating the accident frequency for drift grounding in general and powered grounding for ferries. It was concluded that these discrepancies are related to uncertainties in several areas, specifically in the risk model algorithms, the traffic data, the error and failure probability data, and the historical accident statistics.     

Participatory analysis of accidents and incidents as a tool for increasing safety behaviour in fishermen. A pilot intervention study

Abstract

Although occupational accidents are common in fishery, safety work is often not given priority by the fishermen. The aims of this study were to test a group-based intervention for increased activity in safety work through group discussion of accident/incident experience; to study occurred incidents/accidents and how such events were managed; and to study intervention effects on activity in safety work, risk acceptance and perceived manageability of risks. A sample of men from five crews (11 men) of Swedish fishermen participated. The study had a one group pre-test–post-test design for questionnaire data. The emphasis was on qualitative information collected during the intervention and interviews. The results indicated that accident causes could be appraised as being unmanageable even when technical solutions were possible. Psychological factors may cause incidents not to be documented or discussed. Incident experience seldom leads to preventive measures. Interaction between structural, social and psychological factors seemed to explain this. Questionnaires, observations and interview data suggested that some increase in safety work took place during the intervention. After the discussions the participants perceived risks to be less manageable. The study indicated that, although sensitive to dropout, participative safety interventions in fishery are feasible and may be effective. A longer or more intensive intervention may be necessary in order to progress from problem orientation to action orientation.     

Lessons learned from Toulouse and Buncefield disasters: from risk analysis failures to the identification of atypical scenarios through a better knowledge management

The recent occurrence of severe major accidents has brought to light flaws and limitations of hazard identification (HAZID) processes performed for safety reports, as in the accidents at Toulouse (France) and Buncefield (UK), where the accident scenarios that occurred were not captured by HAZID techniques. This study focuses on this type of atypical accident scenario deviating from normal expectations. The main purpose is to analyze the examples of atypical accidents mentioned and to attempt to identify them through the application of a well-known methodology such as the bow-tie analysis. To these aims, the concept of atypical event is accurately defined. Early warnings, causes, consequences, and occurrence mechanisms of the specific events are widely studied and general failures of risk assessment, management, and governance isolated. These activities contribute to outline a set of targeted recommendations, addressing transversal common deficiencies and also demonstrating how a better management of knowledge from the study of past events can support future risk assessment processes in the identification of atypical accident scenarios. Thus, a new methodology is not suggested; rather, a specific approach coordinating a more effective use of experience and available information is described, to suggest that lessons to be learned from past accidents can be effectively translated into actions of prevention.     

Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network

Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow-ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.     

Human Error Contribution in Collision and Grounding of Oil Tankers

The purpose of this article is to present a quantitative analysis of the human failure contribution in the collision and/or grounding of oil tankers, considering the recommendation of the “Guidelines for Formal Safety Assessment” of the International Maritime Organization. Initially, the employed methodology is presented, emphasizing the use of the technique for human error prediction to reach the desired objective. Later, this methodology is applied to a ship operating on the Brazilian coast and, thereafter, the procedure to isolate the human actions with the greatest potential to reduce the risk of an accident is described. Finally, the management and organizational factors presented in the “International Safety Management Code” are associated with these selected actions. Therefore, an operator will be able to decide where to work in order to obtain an effective reduction in the probability of accidents. Even though this study does not present a new methodology, it can be considered as a reference in the human reliability analysis for the maritime industry, which, in spite of having some guides for risk analysis, has few studies related to human reliability effectively applied to the sector.     

Organizational factors affecting the incident reporting of train drivers

The under-reporting of accidents and incidents is well established as a substantial problem across industries; however, few studies have specifically examined employees' reasons for failing to report. This study investigates the factors affecting incident reporting by train drivers (n = 128) in three different areas of British Rail (the company that operated the railway network in Britain, prior to its privatization in 1994). Drivers completed a questionnaire rating their likelihood of reporting a number of hazardous incidents and giving their reasons for not reporting them. Intention not to report incidents was predicted by 'managers take no notice' of reports and the interaction between 'just part of the day's work' and 'nothing would get done'. It is concluded that incident reporting is most influenced by the way that drivers perceive managers' reactions to reports. In addition, the three areas differed significantly in their likelihood of reporting incidents, suggesting that the local subculture has an important influence on intentions to report, with negative perceptions of managers' attitudes suppressing the reporting of incidents.     

Making sense of pragmatic and charismatic leadership stories: Effects on vision formation

For some time, it has been argued that stories articulated by leaders are an important vehicle for exercising influence, but stories of leadership might also serve as a means for developing leadership potential. One critical activity involved in leadership is vision formation, which involves constructing and communicating a future state that guides followers in “making sense” of complex organizational events. Like leader visions, analyzing stories also, by nature, evokes sensemaking processes. As a result, analyzing stories of leadership may provide a natural means for practicing the art of sensemaking. In the present investigation, undergraduates were asked to read six short stories about incidents of either pragmatic or charismatic leadership in business settings. After reading each story, questions were asked to encourage sensemaking of story events, causes, and emotions. Participants were subsequently asked to formulate visions for leading a secondary school –– a transfer task. It was found that stronger visions were produced when participants were asked to analyze both story events and the causes of these events. The implications of these findings for the use of leadership stories in leader development initiatives are discussed.     

Reduce rework,improve safety: an empirical inquiry into the precursors to error in construction

A positive association between rework and safety events that arise during the construction process has been identified. In-depth semi-structured interviews with operational and project-related employees from an Australian construction organisation were undertaken to determine the precursors to rework and safety events. The analysis enabled the precursors of error to examined under the auspices of: (1) People, (2) Organisation, and (3) Project. It is revealed that the precursors to error for rework and safety incidents were similar. A conceptual framework to simultaneously reduce rework and safety incidents is proposed. It is acknowledged that there is no panacea that can be used to prevent rework from occurring, but from the findings presented indicate that a shift from a position of ‘preventing’ to ‘managing’ errors is required to enable learning to become an embedded feature of an organisation’s culture. As a consequence, this will contribute to productivity and performance improvements being realised.     

Benchmarking Safety Climate in Hazardous Environments: A Longitudinal, Interorganizational Approach

Safety climate is an important element of organizational reliability. This study applied benchmarking strategies for monitoring safety climate across nine North Sea oil and gas installations that were surveyed in consecutive years. Examination of absolute changes in safety climate complemented the benchmarking approach. Discriminant function analyses (DFA) identified the elements of safety climate predictive of self-reported accidents; correlational analyses were applied to the scale scores and accident proportions across the year period. Absolute improvements were substantial, with safety climate profiles converging in the second year. Large relative improvements were also observed. DFA highlighted perceived management commitment to safety and willingness to report accidents as significant predictors of personal accident involvement. Changes in perceived management commitment to safety were closely associated with changes in safety behavior.     

The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels

There is a long history of studying the relationship between weather and maritime activities. This article analyzes the link between relative incident rate (RIR) and general weather factors within certain gridded areas and time periods. The study area, which encompasses a broad extent of Atlantic Canadian waters, includes fishing incidents recorded by the Canadian Coast Guard from 1997 to 1999. Methodologies used for traffic track generation in a geographical information system and aggregation of all relevant weather data needed for the statistical analyses are presented. Ultimately, a regression tree was built to illustrate the relationship between incident rate and the following six weather factors: wave height; sea surface temperature; air temperature; ice concentration; fog presence; and precipitation. Results from the regression tree reveal that the RIR defined as (incident number per area-day)/(traffic amount per area-day) across grid cells with incidents, increases as the weather conditions deteriorate in a general way, and the concentration of ice has the biggest influence on the magnitude of incident rates for a given level of traffic exposure. The results from this analysis may assist administrators of maritime traffic, especially those associated with fishing activities, through a better understanding of the influence on RIR of certain weather conditions within given areas in specific time periods.     

Of Disasters and Dragon Kings: A Statistical Analysis of Nuclear Power Incidents and Accidents

We perform a statistical study of risk in nuclear energy systems. This study provides and analyzes a data set that is twice the size of the previous best data set on nuclear incidents and accidents, comparing three measures of severity: the industry standard International Nuclear Event Scale, the Nuclear Accident Magnitude Scale of radiation release, and cost in U.S. dollars. The rate of nuclear accidents with cost above 20 MM 2013 USD, per reactor per year, has decreased from the 1970s until the present time. Along the way, the rate dropped significantly after Chernobyl (April 1986) and is expected to be roughly stable around a level of 0.003, suggesting an average of just over one event per year across the current global fleet. The distribution of costs appears to have changed following the Three Mile Island major accident (March 1979). The median cost became approximately 3.5 times smaller, but an extremely heavy tail emerged, being well described by a Pareto distribution with parameter α = 0.5–0.6. For instance, the cost of the two largest events, Chernobyl and Fukushima (March 2011), is equal to nearly five times the sum of the 173 other events. We also document a significant runaway disaster regime in both radiation release and cost data, which we associate with the “dragon‐king” phenomenon. Since the major accident at Fukushima (March 2011) occurred recently, we are unable to quantify an impact of the industry response to this disaster. Excluding such improvements, in terms of costs, our range of models suggests that there is presently a 50% chance that (i) a Fukushima event (or larger) occurs every 60–150 years, and (ii) that a Three Mile Island event (or larger) occurs every 10–20 years. Further—even assuming that it is no longer possible to suffer an event more costly than Chernobyl or Fukushima—the expected annual cost and its standard error bracket the cost of a new plant. This highlights the importance of improvements not only immediately following Fukushima, but also deeper improvements to effectively exclude the possibility of “dragon‐king” disasters. Finally, we find that the International Nuclear Event Scale (INES) is inconsistent in terms of both cost and radiation released. To be consistent with cost data, the Chernobyl and Fukushima disasters would need to have between an INES level of 10 and 11, rather than the maximum of 7.     

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.     

Empirical Analysis of the Effects of Cyber Security Incidents

We analyze the time series associated with web traffic for a representative set of online businesses that have suffered widely reported cyber security incidents. Our working hypothesis is that cyber security incidents may prompt (security conscious) online customers to opt out and conduct their business elsewhere or, at the very least, to refrain from accessing online services. For companies relying almost exclusively on online channels, this presents an important business risk. We test for structural changes in these time series that may have been caused by these cyber security incidents. Our results consistently indicate that cyber security incidents do not affect the structure of web traffic for the set of online businesses studied. We discuss various public policy considerations stemming from our analysis.     

System of Systems Engineering and Risk Management of Extreme Events: Concepts and Case Study

The domain of risk analysis is expanded to consider strategic interactions among multiple participants in the management of extreme risk in a system of systems. These risks are fraught with complexity, ambiguity, and uncertainty, which pose challenges in how participants perceive, understand, and manage risk of extreme events. In the case of extreme events affecting a system of systems, cause‐and‐effect relationships among initiating events and losses may be difficult to ascertain due to interactions of multiple systems and participants (complexity). Moreover, selection of threats, hazards, and consequences on which to focus may be unclear or contentious to participants within multiple interacting systems (ambiguity). Finally, all types of risk, by definition, involve potential losses due to uncertain events (uncertainty). Therefore, risk analysis of extreme events affecting a system of systems should address complex, ambiguous, and uncertain aspects of extreme risk. To accomplish this, a system of systems engineering methodology for risk analysis is proposed as a general approach to address extreme risk in a system of systems. Our contribution is an integrative and adaptive systems methodology to analyze risk such that strategic interactions among multiple participants are considered. A practical application of the system of systems engineering methodology is demonstrated in part by a case study of a maritime infrastructure system of systems interface, namely, the Straits of Malacca and Singapore.     

Human and organizational factors in offshore safety

The role of human and organizational factors in predicting accidents and incidents has become of major interest to the UK offshore oil and gas industry. Some of these factors had been measured in an earlier study focusing on the role of risk perception in determining accident involvement. The current study sought to extend the methodology by focusing on perceptions of organizational factors that could have an impact on safety. A self-report questionnaire was developed and distributed to 11 installations operating on the UK Continental Shelf. A total of 722 were returned (33% response rate) from a representative sample of the offshore workforce on these installations. The study investigated the underlying structure and content of offshore employees' attitudes to safety, feelings of safety and satisfaction with safety measures. Correlations and step-wise regression analysis were used to test the relationships between measures. The results suggest that 'unsafe' behaviour is the 'best' predictor of accidents/near misses as measured by self-report data and that unsafe behaviour is, in turn, driven by perceptions of pressure for production.     

Human and organizational factors in offshore safety

The role of human and organizational factors in predicting accidents and incidents has become of major interest to the UK offshore oil and gas industry. Some of these factors had been measured in an earlier study focusing on the role of risk perception in determining accident involvement. The current study sought to extend the methodology by focusing on perceptions of organizational factors that could have an impact on safety. A self-report questionnaire was developed and distributed to 11 installations operating on the UK Continental Shelf. A total of 722 were returned (33% response rate) from a representative sample of the offshore workforce on these installations. The study investigated the underlying structure and content of offshore employees' attitudes to safety, feelings of safety and satisfaction with safety measures. Correlations and step-wise regression analysis were used to test the relationships between measures. The results suggest that 'unsafe' behaviour is the 'best' predictor of accidents/near misses as measured by self-report data and that unsafe behaviour is, in turn, driven by perceptions of pressure for production.