Human and organisational factors in maritime accidents: Analysis of collisions at sea using the HFACS

Over the last decade, the shipping industry has implemented a number of measures aimed at improving its safety level (such as new regulations or new forms of team training). Despite this evolution, shipping accidents, and particularly collisions, remain a major concern. This paper presents a modified version of the Human Factors Analysis and Classification System, which has been adapted to the maritime context and used to analyse human and organisational factors in collisions reported by the Marine Accident and Investigation Branch (UK) and the Transportation Safety Board (Canada).     

Implementing the Bayesian paradigm in risk analysis

The Bayesian paradigm comprises a unified and consistent framework for analyzing and expressing risk. Yet, we see rather few examples of applications where the full Bayesian setting has been adopted with specifications of priors of unknown parameters. In this paper, we discuss some of the practical challenges of implementing Bayesian thinking and methods in risk analysis, emphasizing the introduction of probability models and parameters and associated uncertainty assessments. We conclude that there is a need for a pragmatic view in order to ‘successfully’ apply the Bayesian approach, such that we can do the assignments of some of the probabilities without adopting the somewhat sophisticated procedure of specifying prior distributions of parameters. A simple risk analysis example is presented to illustrate ideas.     

Understanding the structure of risk belief systems concerning drone delivery: A network analysis

While e-commerce industries envision drones as a promising solution to the challenges associated with last-mile logistics of product delivery, there is increasing public concern over the risks of these airborne innovations. A growing number of studies seek to gauge the public's risk beliefs about drone delivery and inform policy development before its widespread uptake. Complicating these efforts is the fact that beliefs are interconnected and embedded in a cognitive system. This article argues that public outcomes are not based on atomized and isolated beliefs about risks involved in drone delivery, but instead emerge from the patterns in the relationships among these expressed beliefs. However, little is known about the structural characteristics of risk belief systems related to drone delivery, and the implications for risk communication and management. In an effort to fill this void, we conducted a network analysis of risk beliefs about drone delivery based on nationally representative data (N = 1465). The results revealed structural connections among 11 risk beliefs and explored structural variations in the risk belief system between people with different attitudes toward drone delivery. The simulation results showed that risk-mitigating messages instigated greater changes to the public's risk perceptions when they targeted structurally central, as opposed to peripheral, risk beliefs. The reported evidence adds new insights into how risks about drone delivery are configured in the public consciousness, and provides guidance into how practitioners may mobilize structural leverage to optimize the effectiveness and efficiency of risk communication strategies.     

Cyclist activity and injury risk analysis at signalized intersections: A Bayesian modelling approach

This study proposes a two-equation Bayesian modelling approach to simultaneously study cyclist injury occurrence and bicycle activity at signalized intersections as joint outcomes. This approach deals with the potential presence of endogeneity and unobserved heterogeneities and is used to identify factors associated with both cyclist injuries and volumes. Its application to identify high-risk corridors is also illustrated. Montreal, Quebec, Canada is the application environment, using an extensive inventory of a large sample of signalized intersections containing disaggregate motor-vehicle traffic volumes and bicycle flows, geometric design, traffic control and built environment characteristics in the vicinity of the intersections. Cyclist injury data for the period of 2003–2008 is used in this study. Also, manual bicycle counts were standardized using temporal and weather adjustment factors to obtain average annual daily volumes. Results confirm and quantify the effects of both bicycle and motor-vehicle flows on cyclist injury occurrence. Accordingly, more cyclists at an intersection translate into more cyclist injuries but lower injury rates due to the non-linear association between bicycle volume and injury occurrence. Furthermore, the results emphasize the importance of turning motor-vehicle movements. The presence of bus stops and total crosswalk length increase cyclist injury occurrence whereas the presence of a raised median has the opposite effect. Bicycle activity through intersections was found to increase as employment, number of metro stations, land use mix, area of commercial land use type, length of bicycle facilities and the presence of schools within 50–800 m of the intersection increase. Intersections with three approaches are expected to have fewer cyclists than those with four. Using Bayesian analysis, expected injury frequency and injury rates were estimated for each intersection and used to rank corridors. Corridors with high bicycle volumes, located mainly in the central neighbourhoods of Montreal, have lower risk of injury. These results may reflect the “safety in numbers” hypothesis or cyclist preference towards safer intersections and corridors. Despite these corridors having a lower individual risk, they are nevertheless associated with a greater number of injuries.     

Incorporating organizational factors into Probabilistic Risk Assessment (PRA) of complex socio-technical systems: A hybrid technique formalization

This paper is a result of a research with the primary purpose of extending Probabilistic Risk Assessment (PRA) modeling frameworks to include the effects of organizational factors as the deeper, more fundamental causes of accidents and incidents. There have been significant improvements in the sophistication of quantitative methods of safety and risk assessment, but the progress on techniques most suitable for organizational safety risk frameworks has been limited. The focus of this paper is on the choice of “representational schemes” and “techniques.” A methodology for selecting appropriate candidate techniques and their integration in the form of a “hybrid” approach is proposed. Then an example is given through an integration of System Dynamics (SD), Bayesian Belief Network (BBN), Event Sequence Diagram (ESD), and Fault Tree (FT) in order to demonstrate the feasibility and value of hybrid techniques. The proposed hybrid approach integrates deterministic and probabilistic modeling perspectives, and provides a flexible risk management tool for complex socio-technical systems. An application of the hybrid technique is provided in the aviation safety domain, focusing on airline maintenance systems. The example demonstrates how the hybrid method can be used to analyze the dynamic effects of organizational factors on system risk.     

Traffic safety among motorcyclists in Norway: A study of subgroups and risk factors

Although it is widely recognized that motorcyclists have a particularly high accident risk, our knowledge of the mechanisms producing this accident risk is incomplete. The aims of the present paper are to identify subgroups of motorcyclists with a particularly high accident risk and to identify the relevant risk factors at work. The study presented in this paper relies both on a questionnaire (N = 3356) relating rider characteristics, behaviors and accident risk, and analyses of fatal motorcycle accidents (ca. 100) from 2005 to 2008 in Norway. The results reveal that riders of racing replica bikes (sport bikes), and riders younger than 19 years, including especially youths (16–17 years) riding light motorcycles (≤125 cm³), are subgroups of Norwegian motorcyclists with particularly high accident risks. Analysis of fatal motorcycle accidents reveals that about half of the fatal accidents involve sport bikes. Nearly all fatal accidents with sport bikes involved excessive speed. The combination of low age, low experience, risky behavior and “unsafe” attitudes seems to be a particular potent risk factor for Norwegian motorcyclists.     

Combining task analysis and fault tree analysis for accident and incident analysis: A case study from Bulgaria

Understanding the reasons for incident and accident occurrence is important for an organization's safety. Different methods have been developed to achieve this goal. To better understand the human behaviour in incident occurrence we propose an analysis concept that combines Fault Tree Analysis (FTA) and Task Analysis (TA). The former method identifies the root causes of an accident/incident, while the latter analyses the way people perform the tasks in their work environment and how they interact with machines or colleagues. These methods were complemented with the use of the Human Error Identification in System Tools (HEIST) methodology and the concept of Performance Shaping Factors (PSF) to deepen the insight into the error modes of an operator's behaviour. HEIST shows the external error modes that caused the human error and the factors that prompted the human to err. To show the validity of the approach, a case study at a Bulgarian Hydro power plant was carried out. An incident – the flooding of the plant's basement – was analysed by combining the afore-mentioned methods. The case study shows that Task Analysis in combination with other methods can be applied successfully to human error analysis, revealing details about erroneous actions in a realistic situation.     

Corridor-level signalized intersection safety analysis in Shanghai,China using Bayesian hierarchical models

Most traffic crashes in Chinese cities occur at signalized intersections. Research on the intersection safety problem in China is still in its early stage. The recent development of an advanced traffic information system in Shanghai enables in-depth intersection safety analyses using road design, traffic operation, and crash data. In Shanghai, the road network density is relatively high and the distance between signalized intersections is small, averaging about 200 m. Adjacent signalized intersections located along the same corridor share similar traffic flows, and signals are usually coordinated. Therefore, when studying intersection safety in Shanghai, it is essential to account for intersection correlations within corridors. In this study, data for 195 signalized intersections along 22 corridors in the urban areas of Shanghai were collected. Mean speeds and speed variances of corridors were acquired from taxis equipped with Global Positioning Systems (GPS). Bayesian hierarchical models were applied to identify crash risk factors at both the intersection and the corridor levels. Results showed that intersections along corridors with lower mean speeds were associated with fewer crashes than those with higher speeds, and those intersections along two-way roads, under elevated roads, and in close proximity to each other, tended to have higher crash frequencies.     

Effectiveness of maritime safety control in different navigation zones using a spatial sequential DEA model: Yangtze River case

This paper aims to analyze the effectiveness of maritime safety control from the perspective of safety level along the Yangtze River with special considerations for navigational environments. The influencing variables of maritime safety are reviewed, including ship condition, maritime regulatory system, human reliability and navigational environment. Because the former three variables are generally assumed to be of the same level of safety, this paper focuses on studying the impact of navigational environments on the level of safety in different waterways. An improved data envelopment analysis (DEA) model is proposed by treating the navigational environment factors as inputs and ship accident data as outputs. Moreover, because the traditional DEA model cannot provide an overall ranking of different decision making units (DMUs), the spatial sequential frontiers and grey relational analysis are incorporated into the DEA model to facilitate a refined assessment. Based on the empirical study results, the proposed model is able to solve the problem of information missing in the prior models and evaluate the level of safety with a better accuracy. The results of the proposed DEA model are further compared with an evidential reasoning (ER) method, which has been widely used for level of safety evaluations. A sensitivity analysis is also conducted to better understand the relationship between the variation of navigational environments and level of safety. The sensitivity analysis shows that the level of safety varies in terms of traffic flow. It indicates that appropriate traffic control measures should be adopted for different waterways to improve their safety. This paper presents a practical method of conducting maritime level of safety assessments under dynamic navigational environment.     

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.     

Road risk analysis due to the transportation of chlorine in Rosario city

A risk assessment for chlorine releases as a consequence of truck accidents along the circumvallation highway around Rosario city is presented. A methodology capable of handling uncertainties and an adapted computational system based on the DEGADIS model is developed. Wind intensity, wind direction and atmospheric stability are treated as uncertain parameters. This is critical for the vulnerability calculation. As a result, chlorine concentration distributions and the risk in the area of interest are achieved.     

Human factors, human reliability and risk assessment in license renewal of a nuclear power plant

This paper describes human factors and human reliability assessments carried out as a part of operating license renewal of a nuclear power plant. The structure and contents of human factors assessments, the source material and the role of probabilistic safety assessment are described. Similar evaluations are recommended as an integral part of periodic safety reviews of regulated industrial facilities.The qualitative part of the human factors review is structured according to an international guide. The assessments are here enhanced with operating experience evaluations, measured by quantitative statistical data obtained from inspections and assessments made by plant safety and quality assurance personnel, by regulatory authorities and by peer reviews.The quantitative assessment is based on the roles and contributions of human errors in the accident risk of the target plant. The assessment uses importance measures quantified in probabilistic risk assessment. The scope and the quality of the risk assessment and the scope and the quality of human reliability assessments are also taken into account. Furthermore, the assessment describes how risk assessment can be used to reduce errors and improve human factors. The results tend to be very plant-specific, and the errors have very different importances in different operating states and for different initiating event categories. The results are useful for planning preventive actions, i.e. for preventing errors by developing and prioritizing human factors improvement activities.     

A Bayesian Network to Ease Knowledge Acquisition of Causal Dependence in CREAM: Application of Recursive Noisy‐OR Gates

Cognitive Reliability and Error Analysis Method (CREAM) is a common Human Reliability Analysis (HRA) method of second generation. In this paper, to improve the capabilities of CREAM, we propose a probabilistic method based on Bayesian Network (BN) to determine control mode and quantify Human Error Probability (HEP). The BN development process is described in a four‐phase methodology including (i) definition of the nodes and their states; (ii) building the graphical structure; (iii) quantification of BN through assessment of the Conditional Probability Tables (CPT) values and (iv) model validation. Intractability of knowledge acquisition of large CPTs is the most significant limitation of existing BN model of CREAM. So, the main contribution of this paper lies in its application of Recursive Noisy‐OR (RN‐OR) gate to treat large CPTs assessment and ease knowledge acquisition. RN‐OR allows combination of dependent Common Performance Conditions (CPCs). Finally, a quantitative HEP analysis is applied to enable more precise estimation of HEP through a probabilistic approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Routes to failure: analysis of 41 civil aviation accidents from the Republic of China using the human factors analysis and classification system

The human factors analysis and classification system (HFACS) is based upon Reason's organizational model of human error. HFACS was developed as an analytical framework for the investigation of the role of human error in aviation accidents, however, there is little empirical work formally describing the relationship between the components in the model. This research analyses 41 civil aviation accidents occurring to aircraft registered in the Republic of China (ROC) between 1999 and 2006 using the HFACS framework. The results show statistically significant relationships between errors at the operational level and organizational inadequacies at both the immediately adjacent level (preconditions for unsafe acts) and higher levels in the organization (unsafe supervision and organizational influences). The pattern of the 'routes to failure' observed in the data from this analysis of civil aircraft accidents show great similarities to that observed in the analysis of military accidents. This research lends further support to Reason's model that suggests that active failures are promoted by latent conditions in the organization. Statistical relationships linking fallible decisions in upper management levels were found to directly affect supervisory practices, thereby creating the psychological preconditions for unsafe acts and hence indirectly impairing the performance of pilots, ultimately leading to accidents.     

Reliability and validity of risk analysis

In this paper we investigate to what extent risk analysis meets the scientific quality requirements of reliability and validity. We distinguish between two types of approaches within risk analysis, relative frequency-based approaches and Bayesian approaches. The former category includes both traditional statistical inference methods and the so-called probability of frequency approach. Depending on the risk analysis approach, the aim of the analysis is different, the results are presented in different ways and consequently the meaning of the concepts reliability and validity are not the same.     

A risk analysis of winter navigation in Finnish sea areas

Winter navigation is a complex but common operation in north-European sea areas. In Finnish waters, the smooth flow of maritime traffic and safety of vessel navigation during the winter period are managed through the Finnish–Swedish winter navigation system (FSWNS). This article focuses on accident risks in winter navigation operations, beginning with a brief outline of the FSWNS. The study analyses a hazard identification model of winter navigation and reviews accident data extracted from four winter periods. These are adopted as a basis for visualizing the risks in winter navigation operations. The results reveal that experts consider ship independent navigation in ice conditions the most complex navigational operation, which is confirmed by accident data analysis showing that the operation constitutes the type of navigation with the highest number of accidents reported. The severity of the accidents during winter navigation is mainly categorized as less serious. Collision is the most typical accident in ice navigation and general cargo the type of vessel most frequently involved in these accidents. Consolidated ice, ice ridges and ice thickness between 15 and 40 cm represent the most common ice conditions in which accidents occur. Thus, the analysis presented in this article establishes the key elements for identifying the operation types which would benefit most from further safety engineering and safety or risk management development.     

The hierarchical Tobit model: A case study in Bayesian computing

In this paper we discuss the potentials of a new Bayesian inference tool, called the Gibbs sampler, for the analysis of the censored regression or Tobit model. Tobit models have a wide range of applications in empirical sciences, like econometrics and biometrics. The estimation results of the simple Tobit model will be compared to a hierarchical Tobit model, and the Gibbs sampling approach to the related classical algorithm of expectation-maximisation (EM). The underlying botanical example of this paper is concerned with the censoring mechanism in plant reproduction and proposes the Bayesian Tobit model for the growth relationship between the reproductive part and the rest of the plant.     

A longitudinal study of risk factors for motorcycle crashes among junior college students in Taiwan

A cohort of 4729 junior college students in an urban and a rural area in Taiwan was followed up for a period of 20 months. Students' characteristics, including riding exposures, as well as human, vehicular, and environmental factors were collected using one initial and three follow-up questionnaires. The Anderson-Gill (AG) multiplicative intensity model was used to determine the risk of a motorcycle crash over time while also allowing for the modeling of multiple events. The average response rate for the four assessments was 92%. The adjusted relative hazard (RH) for students living in the rural as opposed to the urban area for crashes was 1.67 at the beginning of the study but decreased to 0.66 by the end. Past motorcycle crash history, number of riding days, average riding distance, risk-taking level, alcohol consumption, and traffic violations were all significantly associated with an increased risk of being involved in a crash. Conversely, increasing age, riding experience, and automobile licensure were related to a decreased risk of crashing. Furthermore, helmet use was not independently related to the risk of crashing. In conclusion, a high-risk group predisposed to involvement in a motorcycle crash, including both non-injury and injury-related crashes, can be identified using selected risk factors for crash prevention among young riders.     

An aftermath analysis of the 2014 coal mine accident in Soma,Turkey: Use of risk performance indicators based on historical experience

On the 13th of May 2014 a fire related incident in the Soma coal mine in Turkey caused 301 fatalities and more than 80 injuries. This has been the largest coal mine accident in Turkey, and in the OECD country group, so far. This study investigated if such a disastrous event should be expected, in a statistical sense, based on historical observations. For this purpose, PSI's ENSAD database is used to extract accident data for the period 1970–2014. Four different cases are analyzed, i.e., OECD, OECD w/o Turkey, Turkey and USA. Analysis of temporal trends for annual numbers of accidents and fatalities indicated a non-significant decreasing tendency for OECD and OECD w/o Turkey and a significant one for USA, whereas for Turkey both measures showed an increase over time. The expectation analysis revealed clearly that an event with the consequences of the Soma accident is rather unlikely for OECD, OECD w/o Turkey and USA. In contrast, such a severe accident has a substantially higher expectation for Turkey, i.e. it cannot be considered an extremely rare event, based on historical experience. This indicates a need for improved safety measures and stricter regulations in the Turkish coal mining sector in order to get closer to the rest of OECD.     

Bayesian analysis of computer code outputs: A tutorial

The Bayesian approach to quantifying, analysing and reducing uncertainty in the application of complex process models is attracting increasing attention amongst users of such models. The range and power of the Bayesian methods is growing and there is already a sizeable literature on these methods. However, most of it is in specialist statistical journals. The purpose of this tutorial is to introduce the more general reader to the Bayesian approach.