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.     

Cost-Benefit Analysis of All-Terrain Vehicles at the CPSC1

The present value of the costs of injuries and deaths associated with each unit of a product is a useful number in conducting cost-benefit analysis at the CPSC. Before a remedy is known, this value indicates the approximate amount that it is worth spending on a fix; similarly, if a fix is proposed, this number indicates whether it is cost-justified. The method of analysis is applied to all-terrain vehicles. Injury costs per vehicle are estimated. Determinants of accidents are estimated using a logit regression model applied to surveys of users and of accident victims; this indicates factors associated with accidents, and is used to estimate marginal probabilities. Costs of various vehicle components are estimated using a hedonic price index. This information (injury costs, accident probabilities, and component cost) is combined in a benefit–cost analysis of proposed remedies.     

Improvement of Highway Safety I: Identification of Causal Factors Through Fault-Tree Modeling1

As the first article of a two-part series, the purpose of this paper is to examine the functional factors that contribute to automobile accident occurrence and to model the causation structure in the form of a fault-tree. The fault-tree model provides an intuitive framework for qualitatively decomposing possible pathways to accident occurrence. Fault-tree analysis also provides a statistical representation of how interacting driver, vehicle, and environmental factors contribute to the likelihood of automobile accident occurrence. The application of this model facilitates pinpointing those factors that most contribute to accident causation and subsequently enables the identification and comparison of potential crash avoidance technologies.     

The Health Impact of Major Nuclear Accidents: The Case Of Greece

An assessment of the radiological consequences that would result for the population of Greece from postulated major nuclear accidents in the Kozloduy nuclear power station in Bulgaria is performed. Kozloduy lies at a distance of 225 km from the northern borders of Greece and contains six reactors, all of the Russian WWER type. The postulated accidents that are classified as level 7 accidents on the International Nuclear Event Scale, involve significant releases of radioactive materials into the environment, and widespread health and environmental effects. The analysis is performed by the MACCS code. The estimated consequences are compared to the corresponding actual impact of the Chernobyl accident in Greece. The results of the analysis indicate that, under the conservative assumptions adopted, the radiological consequences of the most severe accidents considered would be about 1.5 orders of magnitude larger than the actual radiological consequences of the Chernobyl accident.     

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.     

Analyzing Collision,Grounding, and Sinking Accidents Occurring in the Black Sea Utilizing HFACS and Bayesian Networks

This study examines and analyzes marine accidents that have occurred over the past 20 years in the Black Sea. Geographic information system, human factor analysis and classification system (HFACS), and Bayesian network models are used to analyze the marine accidents. The most important feature distinguishing this study from other studies is that this is the first study to analyze accidents that have occurred across the whole Black Sea. Another important feature is the application of a new HFACS structure to reveal accident formation patterns. The results of this study indicate that accidents occurred in high concentrations in coastal regions of the Black Sea, especially in the Kerch Strait, Novorossiysk, Kilyos, Constanta, Riva, and Batumi regions. The formation of grounding and sinking accidents has been found to be similar in nature; the use of inland and old vessels has been highlighted as important factors in sinking and grounding incidents. However, the sequence of events leading to collision-contact accidents differs from the sequence of events resulting in grounding and sinking accidents. This study aims to provide information to the maritime industry regarding the occurrence of maritime incidents in the Black Sea, in order to assist with reduction and prevention of the marine accidents.     

The Roads Ahead: Collision Risks, Trends, and Safety of Drivers

The propensity of fatal traffic collisions transcends driver age and reinforces the need to evaluate, among other factors, the impact of roadway lighting and other features of driver vision, perception, and performance. Collisions may result from a driver's inability to notice delineation, recognize warnings, and other possible road safety controls during various lighting conditions. Hence we compare the relative accident involvement ratio (RAIR) of collisions of millions of drivers from two U.S. States over an 11-year period, 1991–2001. We associate collision trends through RAIR with bathtub curves that are commonly identified with product failure and reliability engineering. Hence we observe the need for improved and automated driver's license testing techniques and applications, especially as these relate to the visual and cognitive abilities of drivers of all ages. Our findings may ultimately improve motorist safety, save lives, and benefit numerous other states, countries, and agencies, including, but not limited to, aviation, commercial vehicles, maritime, and rail sectors, among others.     

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.     

Evacuation Risks: A Tentative Approach for Quantification

This study tries to assess the risk of deaths and injuries from motor vehicle accidents associated with an evacuation of population groups in case of nuclear plant accidents. The risk per person–km is evaluated using: (a) data from previous evacuation: information from Soufriere evacuation (Guadeloupe Island 1976) and Mississauga (1979), added to Hans and Sell's data: no road accident occurred for a sample of 1,500,000 persons; (b) national recording system for motor vehicle accident: the rates of 2.2 10 ^-8 deaths per person–km and 32 10^-8 injuries per person–km is calculated as an average. These last rates in France overestimate the number of casualties. A reasonable hypothesis is to assume that the probability of road accident occurrence follows a Poisson distribution, as these events are independent and unfrequent, as no accident was observed in a sample of 1,500,000 persons the probability is between 0 and an upper value of 0.24 10^-8 deaths per person-km and 3.29 10^-8 injuries per person–km. The average and maximum population involved within different radii around French and U.S. Nuclear power sites are taken as a sample size in order to study the total risk of deaths and injuries in the hypothesis of an evacuation being necessary to protect the populations.     

Probabilistic Analysis of Domino Effects by Using a Matrix-Based Simulation Approach

Major industrial accidents occurring at so-called major hazard installations may cause domino accidents which are among the most destructive industrial accidents existing at present. As there may be many hazard installations in an area, a primary accident scenario may potentially propagate from one installation to another, and correlations exist in probability calculations of domino effects. In addition, during the propagation of a domino effect, accidents of diverse types may occur, some of them having a synergistic effect, while others do not. These characteristics make the analytical formulation of domino accidents very complex. In this work, a simple matrix-based modeling approach for domino effect analysis is proposed. Matrices can be used to represent the mutual influences of different escalation vectors between installations. On this basis, an analysis approach for accident propagation as well as a simulation-based algorithm for probability calculation of accidents and accident levels is provided. The applicability and flexibility of this approach is discussed while applying it to estimate domino probabilities in a case study.     

Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks

In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow‐tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures.     

Who by Accident? The Social Morphology of Car Accidents

Prior studies in the sociology of accidents have shown that different social groups have different rates of accident involvement. This study extends those studies by implementing Bourdieu's relational perspective of social space to systematically explore the homology between drivers’ social characteristics and their involvement in specific types of motor vehicle accident. Using a large database that merges official Israeli road‐accident records with socioeconomic data from two censuses, this research maps the social order of road accidents through multiple correspondence analysis. Extending prior studies, the results show that different social groups indeed tend to be involved in motor vehicle accidents of different types and severity. For example, we find that drivers from low socioeconomic backgrounds are overinvolved in severe accidents with fatal outcomes. The new findings reported here shed light on the social regularity of road accidents and expose new facets in the social organization of death.     

Safety Impacts of SUVs, Vans, and Pickup Trucks in Two-Vehicle Crashes

Policy makers, vehicle manufacturers, and consumers have shown growing concern about the relative safety of sport utility vehicles (SUVs), vans, pickups, and cars. Empirical analysis of real-world crashes is complicated by the possibility that apparent relationships between vehicle type and safety may be confounded by other factors, such as driver behavior and crash circumstances. This study compares different vehicle types with respect to their crashworthiness (self-protection) and aggressivity (risk to others) in crashes between two passenger vehicles. The U.S. Crashworthiness Data System is used to analyze detailed information on 6,481 drivers involved in crashes during 1993-1999. Logistic regression analysis is used to model the risk of serious injury or death to a driver, conditional on a crash occurring. Covariates include the body type of each vehicle in the crash; the driver's age, gender, and restraint use; and the configuration of the crash. A unique feature of this study is the use of "delta-v" to represent the joint effects of vehicle mass and crash severity. While estimated effects are somewhat sensitive to the injury severity level used as the outcome variable, SUVs, vans, and pickups appear to be more aggressive and may be more crashworthy than cars. Effects of pickups are most pronounced. Drivers in pickups face less risk of serious injury than car drivers (odds ratio [OR], 0.35; 95% confidence interval [CI], 0.20-0.60), and drivers who collide with pickups experience more than twice the risk than those who collide with a car (OR, 2.18; 95% CI, 1.03-4.62). While vehicle mass and crash severity contribute to the apparent crashworthiness and aggressivity of passenger vehicles, other vehicle characteristics associated with body type (e.g., the stiffness and height of the underlying structure of the vehicle) also influence safety risks.     

Individual differences in working hours, work perceptions and accident rates in veterinary surgeries

This study addresses an under-researched outcome of occupational stress, namely the frequency of accidents at work and car accidents. A large, random sample of 778 vets and their auxiliary personnel was assessed in relation to socio-demographic variables, work perception and accident involvement. The study, involving veterinary practices in Germany, revealed that work-related injuries/accidents experienced during the previous 12-month period were significantly related to individual differences in job-related stress and job satisfaction. The average yearly number of car accidents was predicted by age, working climate and job satisfaction. Furthermore, driving accidents going to or from work during the previous year were calculated against distance travelled. Vets working in excess of 48 h/week displayed significantly more driving accidents when visiting clients. Results indicate heterogeneous correlations both between and within the two categories of car and work accidents, suggesting that these are different phenomena, each with its own associated personal and work factors. Results also suggest that work perceptions may act as intermediary influences on accident rates.     

Individual differences in working hours,work perceptions and accident rates in veterinary surgeries

This study addresses an under-researched outcome of occupational stress, namely the frequency of accidents at work and car accidents. A large, random sample of 778 vets and their auxiliary personnel was assessed in relation to socio-demographic variables, work perception and accident involvement. The study, involving veterinary practices in Germany, revealed that work-related injuries/accidents experienced during the previous 12-month period were significantly related to individual differences in job-related stress and job satisfaction. The average yearly number of car accidents was predicted by age, working climate and job satisfaction. Furthermore, driving accidents going to or from work during the previous year were calculated against distance travelled. Vets working in excess of 48 h/week displayed significantly more driving accidents when visiting clients. Results indicate heterogeneous correlations both between and within the two categories of car and work accidents, suggesting that these are different phenomena, each with its own associated personal and work factors. Results also suggest that work perceptions may act as intermediary influences on accident rates.     

Identifying Dangerous Trucking Firms

The paper develops a statistical procedure for predicting the safety performance of motor carriers based on characteristics of firms and results of two government safety enforcement programs. One program is an audit of management safety practices, and the other is a program to inspect drivers and vehicles at the roadside for compliance with safety regulations. The technique can be used to provide safety regulators with an empirical approach to identify the most dangerous firms and provide a priority list of firms against which educational and enforcement actions should be initiated. The government needs to use such an approach rather than directly observing accident rates because the most dangerous firms are generally small and, despite relatively high accident rates, accidents remain rare events. The technique uses negative-binomial regression procedures on a dataset of 20,000 firms. The definition of poor performance in roadside inspection is based on both the rate of inspections per fleet mile and the average number of violations found during an inspection. This choice was made because selection for inspection has both a random and nonrandom component. The results of the study suggest that both of the government's safety programs help identify the most dangerous firms. The 2.5% of firms that do poorly in both programs have an average accident rate twice that of the mean for all other firms.     

Traffic Accidents and Convictions: Group Totals Versus Rate per Kilometer Driven

Using group totals, young drivers, male drivers, and especially young male drivers are substantially over-represented in terms of both traffic accidents and traffic-related convictions. On the other hand, when accidents and convictions are considered in terms of their rates while the person is actually driving, older drivers and female drivers gain in importance, with the female drivers' traffic accident rate and rates for some convictions exceeding the male drivers' rates. Similarly, the older drivers' accident rate and rates on some convictions exceeded the younger drivers' rates when considered in terms of driving exposure. In addition, these findings lead one to speculate that recent driving exposure more than driver age is the central factor in traffic accident involvement. The implications for policy makers and educators are clear. Although young male drivers are undeniably a legitimate focus for traffic accident countermeasures, they should not be targeted at the expense of drivers who drive less but have as high or higher accident rates—female and older drivers.     

Coordinability and Consistency in Accident Causation and Prevention: Formal System Theoretic Concepts for Safety in Multilevel Systems

Although a “system approach” to accidents in sociotechnical systems has been frequently advocated, formal system theoretic concepts remain absent in the literature on accident analysis and system safety. To address this gap, we introduce the notions of coordinability and consistency from the hierarchical and multilevel systems theory literature. We then investigate the applicability and the importance of these concepts to accident causation and safety. Using illustrative examples, including the worst disaster in aviation history, and recent incidents in the United States of aircraft clipping each other on the tarmac, we propose that the lack of coordinability is a fundamental failure mechanism causing or contributing to accidents in multilevel systems. We make a similar case for the lack of consistency. Coordinability and consistency become ingredients for accident prevention, and their absence fundamental failure mechanisms that can lead to system accidents. Finally, using the concepts introduced in this work, we identify several venues for further research, including the development of a theory of coordination in multilevel systems, the investigation of potential synergies between coordinability, consistency, and the high reliability organizations paradigm, and the possibility of reframing the view that “sloppy management is the root cause of many industrial accidents” as one of lack of coordinability and/or consistency between management and operations. By introducing and expanding on the concepts of coordinability and consistency, we hope to contribute to the thinking about, and the to language of, accident causation, and prevention and to add to the intellectual toolkit of safety professionals and academics.     

Compressed Natural Gas Bus Safety: A Quantitative Risk Assessment

This study assesses the fire safety risks associated with compressed natural gas (CNG) vehicle systems, comprising primarily a typical school bus and supporting fuel infrastructure. The study determines the sensitivity of the results to variations in component failure rates and consequences of fire events. The components and subsystems that contribute most to fire safety risk are determined. Finally, the results are compared to fire risks of the present generation of diesel-fueled school buses. Direct computation of the safety risks associated with diesel-powered vehicles is possible because these are mature technologies for which historical performance data are available. Because of limited experience, fatal accident data for CNG bus fleets are minimal. Therefore, this study uses the probabilistic risk assessment (PRA) approach to model and predict fire safety risk of CNG buses. Generic failure data, engineering judgments, and assumptions are used in this study. This study predicts the mean fire fatality risk for typical CNG buses as approximately 0.23 fatalities per 100-million miles for all people involved, including bus passengers. The study estimates mean values of 0.16 fatalities per 100-million miles for bus passengers only. Based on historical data, diesel school bus mean fire fatality risk is 0.091 and 0.0007 per 100-million miles for all people and bus passengers, respectively. One can therefore conclude that CNG buses are more prone to fire fatality risk by 2.5 times that of diesel buses, with the bus passengers being more at risk by over two orders of magnitude. The study estimates a mean fire risk frequency of 2.2 x 10(-5) fatalities/bus per year. The 5% and 95% uncertainty bounds are 9.1 x 10(-6) and 4.0 x 10(-5), respectively. The risk result was found to be affected most by failure rates of pressure relief valves, CNG cylinders, and fuel piping.