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

Predicting types of human-related maritime accidents with explanations using selective ensemble learning and SHAP method

Maritime accidents frequently lead to severe property damage and casualties, and an accurate and reliable risk prediction model is necessary to help maritime stakeholders assess the current risk situation. Therefore, the present study proposes a hybrid methodology to develop an explainable prediction model for maritime accident types. Based on the advantages of selective ensemble learning method, this study pioneers to introduce a two-stage model selection method, aiming to enhance the predictive accuracy and stability of the model. Then, SHAP (Shapley Additive Explanations) method is integrated to identify effective mapping associations of seafarers' unsafe acts and their risk factors with the prediction results. The results demonstrate that the model developed achieves good prediction performance with an accuracy of 87.50 % and an F1-score of 84.98 %, which benefits stakeholders in assessing the type of maritime accident in advance, so as to make proactive intervention measures.

A machine vision method for the evaluation of ship-to-ship collision risk

The development of ship technology and information technology has been driving the continuous improvement of ship intelligence, with safety being an inevitable requirement in the shipping industry. A machine vision-based ship collision warning method is proposed for high monitoring system cost and limited information acquisition in safety design of autonomous ship navigation. The method combines machine learning with image algorithms. Firstly, the backbone of YOLOv7 detector is replaced by EfficientFormerV2 network to achieve model lightweight while ensuring detection accuracy. Public datasets SeaShips, Flow and self-made ship pictures are combined, and the network is trained on this dataset. StrongSORT is used for target tracking. Secondly, a data fusion algorithm is introduced to determine the target point at the bow-bottom of the ship based on the time-varying attitude of the camera and the time-series features of the bounding boxes. Ship navigation trajectory estimation is performed using image algorithms. Finally, a collision evaluation model is established to calculate the collision risk index. Experimental results demonstrate that the improved YOLOv7 network maintains similar mAP.5 and Recall compared to the original model, while reducing the parameters by 31.2 % and GFLOPs by 58.4 %. The accuracy of target ship trajectory estimation is high, with MAE values below 1.5 % and RMSE values below 2 % in experiments. In ship collision warning experiments, the proposed method accurately identifies navigating vessels, estimates the trajectories, and provides timely warnings for imminent collision accidents. Compared to traditional ship collision warning methods, this paper offers a more intelligent and lightweight solution.

Using HFACS to understand human error in railway dispatcher performance: a study of proactive safety inspection records

This study analyzes 4,095 proactive safety inspection records obtained from a large dispatching centre by utilising the HFACS framework. These proactive safety inspection records offer comprehensive documentation of incidents, capturing major accidents and numerous minor discrepancies and lapses that often go unnoticed in accident reports. The analysis revealed that most incidents were attributed to unsafe actions, primarily skill-based errors and poor decision-making. Additionally, contributing factors such as adverse mental states, personal readiness, and crew resource management were found to play a significant role as preconditions for unsafe acts. Path analyses further established a significant correlation between factors such as unsafe supervision, preconditions for unsafe acts, and the occurrence of unsafe acts. In our discussion, we critically evaluate the strengths and limitations of proactive safety inspection records in safety research. Moreover, we emphasise these findings' potential to enhance safety within the railway industry.

Neurophysiological Assessment of An Innovative Maritime Safety System in Terms of Ship Operators' Mental Workload, Stress, and Attention in the Full Mission Bridge Simulator

The current industrial environment relies heavily on maritime transportation. Despite the continuous technological advances for the development of innovative safety software and hardware systems, there is a consistent gap in the scientific literature regarding the objective evaluation of the performance of maritime operators. The human factor is profoundly affected by changes in human performance or psychological state. The difficulty lies in the fact that the technology, tools, and protocols for investigating human performance are not fully mature or suitable for experimental investigation. The present research aims to integrate these two concepts by (i) objectively characterizing the psychological state of mariners, i.e., mental workload, stress, and attention, through their electroencephalographic (EEG) signal analysis, and (ii) validating an innovative safety framework countermeasure, defined as Human Risk-Informed Design (HURID), through the aforementioned neurophysiological approach. The proposed study involved 26 mariners within a high-fidelity bridge simulator while encountering collision risk in congested waters with and without the HURID. Subjective, behavioral, and neurophysiological data, i.e., EEG, were collected throughout the experimental activities. The results showed that the participants experienced a statistically significant higher mental workload and stress while performing the maritime activities without the HURID, while their attention level was statistically lower compared to the condition in which they performed the experiments with the HURID (all p < 0.05). Therefore, the presented study confirmed the effectiveness of the HURID during maritime operations in critical scenarios and led the way to extend the neurophysiological evaluation of the HFs of maritime operators during the performance of critical and/or standard shipboard tasks.

Fatal fall-from-height accidents: Statistical treatment using the Human Factors Analysis and Classification System - HFACS

INTRODUCTION

The civil construction industry (CCI) is one of the most dangerous sectors for occupational accidents. Studies conducted in several countries show that occupational accidents involving falls from height are the main cause of deaths in recent years.

METHOD

This article analyzed the combinations of causal factors with the highest likelihood of accidents involving falls from height in construction to assist in decision-making. The methodology was divided into four stages: accident collection and sample definition; accident analysis; probability determination; and obtaining the theoretical curve of an accident probability distribution. The methodology was applied to reports of fatal fall-from-height accidents that occurred in the United States between 1997 and 2020.

RESULTS

The results show that among the accidents analyzed, the highest probability of fatality is when a roofer aged between 31 and 44 years performs their activity on a roof between 10:00 and 11:59 am. It is also noted that the three causal factors most present in the accidents were: organizational process (97.7%); poor management of worker resources (96.6%); and organizational climate (95.4%). From the probability distribution curve, 68% of the fatal accidents occurred after reaching between 18 and 34 causal factors present in the HFACS method categories.

Scientific awareness of marine accidents in Europe: A bibliometric and correspondence analysis

Marine accidents are a significant issue that can lead to loss of life, damage to ships and cargo, and harm to the marine environment. To gain better understanding of scientific awareness of marine accidents occurred in European countries, this study conducted a bibliometric and correspondence analysis of the scientific literature. Bibliometric analysis was employed to examine various publications, which were released during the period between 2012 and December 2022. Moreover, correspondence analysis was used to classify and analyze marine accidents based on the severity and the consequence of the accidents. The findings indicate that scientific awareness of researchers in countries where serious and very serious marine accidents occur is also high. However, Norway stands out as the country with the highest scientific awareness of researchers despite experiencing marine accidents with less serious. The most significant factor contributing to the prominence of researchers here is their collaborations with researchers from other countries. Overall, this study sheds light on the need for further research and action to improve marine accident prevention. Collaborative efforts involving researchers, maritime stakeholders, and policymakers are necessary to address the complex challenges of marine accidents and to ensure the maritime safety and protection of the marine environment.

Maritime autonomous surface ships: can we learn from unmanned aerial vehicle incidents using the perceptual cycle model?

Interest in Maritime Autonomous Surface Ships (MASS) is increasing as it is predicted that they can bring improved safety, performance and operational capabilities. However, their introduction is associated with a number of enduring Human Factors challenges (e.g. difficulties monitoring automated systems) for human operators, with their 'remoteness' in shore-side control centres exacerbating issues. This paper aims to investigate underlying decision-making processes of operators of uncrewed vehicles using the theoretical foundation of the Perceptual Cycle Model (PCM). A case study of an Unmanned Aerial Vehicle (UAV) accident has been chosen as it bears similarities to the operation of MASS through means of a ground-based control centre. Two PCMs were developed; one to demonstrate what actually happened and one to demonstrate what should have happened. Comparing the models demonstrates the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when operating from remote control centres. Practitioner Summary: To investigate underlying decision-making processes of operators of uncrewed vehicles using the Perceptual Cycle Model, by using an UAV accident case study. The findings showed the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when monitoring uncrewed systems from remote control centres.

The use of Bayesian Belief Networks (BBNs) to probe deeper into railway safety management systems - Two studies from Great Britain and Italy

The importance of Safety Management Systems (SMS) to the railway industry is underlined by the fact that all organisations operating on UK railways are required by law to have one. Analysing SMSs can provide a reliable systemic tool to identify hazards and weaknesses within complex systems like the railway, making it possible to significantly increase safety, reducing the odds of near misses and accidents. However, there is little empirical research evidence to determine the impact on safety of a structured SMS. The current paper describes two studies which use Bayesian Belief Networks (BBN) to conceptualise SMSs and their impact on front-line performance. The paper presents the usefulness of BBNs to compare complex systems and reconcile cultural differences within the railway industry, identifying factors that are deemed vital within Italy and Britain. The two studies allowed us to identify the most influential factors within a SMS and how they interact with each other, as well as the strength of the identified relationships. A BBN is particularly useful in estimating how changing some of the node states (e.g., by making safety leadership present) affected the other factors. The current study showed that safety leadership has an impact on the SMSs of the British and Italian railway industries.

Utilizing Haddon matrix to assess nonfatal commercial fishing injury factors in Oregon and Washington

BACKGROUND

Commercial fishing is a precarious industry with high fatal and nonfatal injury rates. The Risk Information System of Commercial [RISC] Fishing project at Oregon State University has been tracking both fatal and nonfatal injuries among Oregon and Washington commercial fishermen. We examined the utility of the RISC dataset variables in highlighting injury factors and prevention opportunities.

METHOD

We identified 245 nonfatal commercial fishing injuries in Oregon and Washington (2000-2018) and assessed the top three injury events (contact with objects or equipment, transportation incidents, and slips/trips/falls) using a cross-sectional design. We generated a Haddon matrix for each event type and populated the matrices with injury-associated factors following our a-priori matrix.

RESULTS

We observed 108 nonfatal injuries due to contact with objects. Contact injuries occurred during fishing (40%) with fishing gears (40%), often while hauling the fishing gear (22%). Common injury mechanisms included getting caught in running equipment or machinery (19%) or compressed by shifting objects or equipment (18%). Of the 58 transportation injuries most occurred in catchers (93%) and smaller vessels (1 to 3 crew) (55%). Vessel casualties were common as several vessels struck rocks/bottom (29%) or experienced fire and explosion (19%). The crew was abandoned to water (38%), often due to no raft or raft malfunctions (19%). Slip/trip/fall injuries (n = 43) typically happened during onboard traffic (49%). Such events were largely experienced by the catcher-processors (44%) including large vessels with > 100 crew (28%).

CONCLUSION

The Haddon matrix demonstrated the injury-event timeline and helped to identify potential injury-associated factors. Our injury-specific risk matrices will let commercial fishing stakeholders determine priorities and work with the experts on prevention efforts.

EEG-based analysis for pilots' at-risk cognitive competency identification using RF-CNN algorithm

Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots' SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/β (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), θ/(α + θ) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (α + θ)/β (p = 0.046 < 0.05 in F and p = 0.012 < 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots' cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.

Understanding the systemic influences on maritime pilot decision-making

Maritime incidents occurring during pilotage are of international concern. Maritime pilots control most pilotage operations worldwide, yet despite the safety criticality of their role, research examining pilot decision-making processes during these complex and dynamic operations is scarce. This article describes the findings from two studies that utilised an integrated systems thinking framework to understand how pilots make decisions and what factors are perceived to influence their decisions. Interviews were held with 22 pilots (Study 1) and 17 maritime safety stakeholders (Study 2) in the New Zealand maritime context. The findings illustrate the challenges pilots face during pilotage and provide insights into their decision-making processes and the systemic factors that can be addressed to improve maritime safety. Given the multiple causal pathways to incidents occurring during pilotage identified by this research, it is suggested that multiple systems-wide interventions are needed, which is likely to require a long-term, strategic approach.

Clinical and epidemiological aspects of victims of maritime accidents on the Mar Grande-Salvador crossing, Bahia, Brazil: a case series study

BACKGROUND

Maritime transportation is an activity of vital importance for societies. The Mar Grande-Salvador crossing is an intercity waterway transport line in Brazil that transports 3,500 passengers/day, including residents and tourists. In 2017, an accident on this crossing was considered to be the biggest maritime tragedy in Bahia in the last decade.

OBJECTIVE

To describe the clinical and epidemiology characteristics of victims of this maritime accident, with analysis on bodily injuries, causes of death and means/instruments that caused the fatal injuries.

DESIGN AND SETTING

Case-series study at the Forensic Medical Institute of Bahia, Brazil.

METHODS

Reports on 73 victims who were examined for bodily injury or were necropsied by the Forensic Medical Institute were analyzed. This study was approved by the institution's Research Ethics Committee (protocol 04012218.1.0000.5032).

RESULTS

The victims' mean age was 33.0 years [95% confidence interval, CI, 26.3-47.0]. The mean age of those who died was 43.0 years [95% CI, 30.5-53.5]. Bodily injuries were found in 74% of the victims. The most frequent bodily injuries were ecchymoses among females (69.7%) and abrasions among males (76.2%). Blunt instruments produced most bodily injuries (85.2%). Among the victims who died, 68.4% were female. Mechanical asphyxiation through drowning was the leading cause of death (89.4%). The overall lethality rate was 26%, and this was higher among females (28.2%).

CONCLUSION

Women were the main victims of this maritime accident. Bodily injuries occurred more frequently than death, but these injuries proved to be quite significant, thus demonstrating the importance of measures to improve the safety of navigation.

A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H

Unsafe crew acts (UCAs) related to human errors are the main contributors to maritime accidents. The prediction of unsafe crew acts will provide an early warning for maritime accidents, which is significant to shipping companies. However, there exist gaps between the prediction models developed by researchers and those adopted by practitioners in human risk analysis (HRA) of the maritime industry. In addition, most research regarding human factors of maritime safety has concentrated on hazard identification or accident analysis, but not on early warning of UCAs. This paper proposes a Bayesian network (BN) version of the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method to predict the probability of seafarers' unsafe acts. After the identification of performance-shaping factors (PSFs) that influence seafarers' unsafe acts during navigation, the developed prediction model, which integrates the practicability of SPAR-H and the forward and backward inference functions of BN, is adopted to evaluate the probabilistic risk of unsafe acts and PSFs. The model can also be used when the available information is insufficient. Case studies demonstrate the practicability of the model in quantitatively predicting unsafe crew acts. The method allows evaluating whether a seafarer is capable of fulfilling their responsibility and providing an early warning for decision-makers, thereby avoiding human errors and sequentially preventing maritime accidents. The method can also be considered as a starting point for applying the efforts of HRA researchers to the real world for practitioners.

Have Non-Native English-Speaking Marine Cadet Engineers Been Educated Appropriately?

Freight transport via ships is the cheapest and most effective way to transfer more than 80% of the global cargo volume. Seafarers have always been multinational, and accordingly, non-English-speaking crew members are becoming an increasing presence on board. Although marine engineers comprise half of the crews among all seafarers on board ships, Standard English guides, such as SMCP for navigation officers designed to reduce communication barriers, are unavailable for marine engineers. IMO conventions require marine engineers to possess adequate English skills. However, marine accidents due to inappropriate communication between crew members continue to occur. In this paper, 185 marine engineer cadets enrolled in two universities who had completed 12 months of training on a commercial ship or school training ship were surveyed in terms of the adequacy of English courses for marine engineers in class. This paper investigated whether the marine engineer English subjects are reviewed and analyzed and whether the English examination for the Certificate of Competence is suitable for the content taught in international maritime instruments and for the actual work of engineers. Finally, this paper aimed to establish a need to develop Standard English for engineers.

Human Factor Analysis (HFA) Based on a Complex Network and Its Application in Gas Explosion Accidents

Humans are at the core of the social-technical system, and their behavioral errors affect the reliability and safety of the entire system in varying degrees. Occupational accidents and large-scale industrial accidents are often attributed to human errors, accounting for more than 80% of accidents. In view of the complexity of systems and the coupling of elements, a new HFA method is proposed based on a complex network. According to system safety theory, a complex network is regarded as a network composed of humans, matters, environments, and management, and the basic structure of the HFA network is summarized. On this basis, a system safety method of HFA is developed which proposes a universal human error causation model. Moreover, a network analysis method for human errors is also presented, which is a comprehensive analysis of human errors that have occurred. Finally, the above methods are applied to gas explosion accidents that occurred in China. Results show that the two methods proposed are universal to all fields, and their combination improves the effectiveness of human error management and promotes the targeted, proactive, systematic, and dynamic prevention of critical nodes and paths from a holistic perspective.

Sustainable Operation of Unmanned Ships under Current International Maritime Law

The aim of this paper is to evaluate the current international maritime legislative framework and assess its relevance in sustaining the operations of unmanned ships while addressing the issue of liability from system malfunctions. The paper initially explores the legal definition of a ship and evaluates whether the existence of an on-board crew is an integral part of the definition. Subsequently, the analysis continues with assessing the legal implications and challenges for the sustainable operation of unmanned ships, such as the governing flag state legislation that defines liability parameters, taking into consideration the existing levels of vessel automation. The paper concludes with an evaluation of the contractual issues and potential stakeholder liability related to governing a flag state. In addition, the potential transfer of liability from the ship operators to manufacturers as pertains to unmanned ship operation is also addressed. The concluding remarks suggest that unmanned ship operation is sustainable under the current international maritime legislative framework; however, the current legislation should be considered as a baseline from which specific legislation for the operation of unmanned ships can be drawn. The methodology utilised for this paper is based on the legal doctrine.

Safety Risk Assessment of Prefabricated Buildings Hoisting Construction: Based on IHFACS-ISAM-BN

Prefabricated buildings that are more environmentally friendly have been vigorously promoted by the Chinese government because of the reduced waste and carbon emissions during the construction process. Most of the construction processes of prefabricated buildings are completed in the prefabricated component factory, but the safety risks during the hoisting process cannot be ignored. In this paper, the initial framework of a Bayesian Network (BN) is obtained from the combination of the improved Human Factors Analysis and Classification System Model (HFACS) and BN. The improved similarity aggregation method (SAM) is used to calculate the prior probability of BN, which can better summarize and deal with the fuzzy judgment of experts on risk accidents. The improved SAM can consider both the weight of experts and the relative consistency of their opinions, which is of great significance for improving the reliability of BN inputted data. This paper uses the construction project in Sanya, Hainan Province, to verify the validity of the model. The results show that the calculation results of the model are basically consistent with the actual situation. The safety risk of this project is relatively low, and the premise of unsafe behaviors and unsafe supervision are the key risk factors of the project. In addition to maintaining good construction conditions and workers’ healthy states, it is also necessary to carefully check the performance of tower cranes and equipment such as spreaders. During the operation process of the tower crane, workers should avoid walking or staying within the hoisting range.

Risk Assessment of Ship Navigation in the Northwest Passage: Historical and Projection

Shipping volumes in the Northwest Passage are likely to increase under climate change due to the distance advantage over traditional routes and the special strategic location of the Arctic. However, the harsh environment and poor channel conditions may pose a considerable risk to ship navigation. To ensure the safety of ships, understand the navigability of the route, and plan the sustainable use of the Northwest Passage, it is crucial to provide a quantitative risk assessment. Here, we present an analysis of several natural risks faced by ships in the Northwest Passage based on available datasets and use climate model simulations to project the navigability changes. The results showed that: (1) The sea-ice risk to ships in the Northwest Passage has been significantly reduced over the period 1979–2019, and the risk for Polar Class 6 (PC6) ships has decreased more rapidly than for general open-water (OW) ships. The difference in ice-breaking capacity further affects the seaworthy season, with the second seaworthy month being August for OW ships and October for PC6 ships, in addition to the commonly best September. (2) Low visibility is a more common form of adverse weather than strong wind for navigation in the Northwest Passage, mainly on the northern route, although pilotage conditions appear to be improving in September. (3) According to the comprehensive risk map, the distribution of risk is dominated by sea ice. The southern route of the Northwest Passage is superior to the northern route in terms of both sea ice and weather conditions, but there is a risk of shallow water. (4) For the northern route, which has greater transport potential, projections suggest that the sea-ice risk will be steadily lower than any extreme light ice year observed historically whether for the ship class OW or PC6 by 2050, with an increase of 50–80 navigable days, and the navigable period could be from June to January of the following year for PC6 ships by 2100. Our results provide valuable information for ships planning to pass through the Northwest Passage.

Development and testing of a tool to measure the organizational safety climate aboard US Navy ships

INTRODUCTION

Safety climate is a critical human factor that can increase safety-related behaviors and reduce accidents. This research reports on a three-phase program of development and validation of a safety climate survey tool initiated by U.S. Naval Surface Forces after numerous accidents and near misses.

METHOD

The initial survey was administered to 4,042 sailors aboard 30 warships, and factor analysis supported a three-factor measure of a safety climate composed of operational compliance, positive work environment, and organizational resources. The predictive validity of the newly developed safety climate measure was tested against the number of accidents reported in the 12 months after the safety climate survey.

RESULTS

This analysis revealed that a positive work environment and operational compliance were linked to fewer accidents; surprisingly, organizational resources were linked to more accidents. Implications for future research on safety climate and occupational safety are discussed.

Latent-Cause Extraction Model in Maritime Collision Accidents Using Text Analytics on Korean Maritime Accident Verdicts

Maritime collision accidents occur frequently and result in huge damages. Complex collision accidents are especially associated with worse damages. Complex maritime collision accidents involve other types of accidents barring the main accident, such as fire, explosions, capsizes, sinking, and even casualties. When a maritime accident occurs, the maritime accident verdict covers the surveyed facts from the origin of the accident to the consequences. The survey usually reveals the primary cause of the accident; however, complex causes may remain latent. Therefore, this research aims to apply text analytics to maritime verdicts of collision accident cases to identify the latent causes in complex collision accidents. The proposed methods separated the collected corpus into the training dataset and the test dataset. The word propensity database was extracted from the training dataset and applied to sample verdicts of complex maritime collision accidents in the test dataset. The expected results of this research were words that appeared in only complex maritime accidents with a high propensity for additional categories and the relevant context that explains the latent causes that underlie the complexity of the maritime accident. The conclusion suggested that the latent causes derived should be provided to ships to help prevent future complex collision accidents.

Shipping Accidents Dataset: Data-Driven Directions for Assessing Accident’s Impact and Improving Safety Onboard

Recent tragic marine incidents indicate that more efficient safety procedures and emergency management systems are needed. During the 2014–2019 period, 320 accidents cost 496 lives, and 5424 accidents caused 6210 injuries. Ideally, we need historical data from real accident cases of ships to develop data-driven solutions. According to the literature, the most critical factor to the post-incident management phase is human error. However, no structured datasets record the crew’s actions during an incident and the human factors that contributed to its occurrence. To overcome the limitations mentioned above, we decided to utilise the unstructured information from accident reports conducted by governmental organisations to create a new, well-structured dataset of maritime accidents and provide intuitions for its usage. Our dataset contains all the information that the majority of the marine datasets include, such as the place, the date, and the conditions during the post-incident phase, e.g., weather data. Additionally, the proposed dataset contains attributes related to each incident’s environmental/financial impact, as well as a concise description of the post-incident events, highlighting the crew’s actions and the human factors that contributed to the incident. We utilise this dataset to predict the incident’s impact and provide data-driven directions regarding the improvement of the post-incident safety procedures for specific types of ships.

Estimating Critical Latency Affecting Ship’s Collision in Re-Mote Maneuvering of Autonomous Ships

Estimation of the critical latency that can cause collision in remote maneuvering of autonomous ships can provide a clue to avoid collisions. The concept of estimating the critical latency was established using the turning circle formed by the turning maneuver of the own ship, and critical latency was estimated using the radius of the turning circle with the turning time ratio. The turning circle was observed using the turning trajectory of the give-way vessel measured in the ship maneuvering simulation experiment. Experimental results demonstrated that the proposed method is capable of identifying both the location and time of the collision due to critical latency. As a result, a clue to avoid possible collision in remote maneuvering caused by critical latency was deduced.

Decision making dyads and judgement overconfidence: Implications for high-risk industries

In the workplace, overconfidence is generally considered undesirable as it may increase people's propensity to take risks. In many areas (e.g., aviation, shipping, nuclear control, and driving), risk-taking is detrimental to safety. We hypothesised that decision-makers would be overconfident and, due to group polarisation, decision-making pairs would be more overconfident than single decision-makers. As was predicted, when answering a 24-item general knowledge questionnaire (d = 0.94) and a task exploring how they might reorient themselves if lost (d = 1.93), participants (N = 63) were overconfident about their performance; importantly, participants in pairs (n = 32) were more overconfident on general knowledge (Hedges' g = 0.51) and lost procedures (Hedges' g = 0.52), than were participants who completed the tasks alone (n = 31). The findings imply that in some situations, single decision-makers may exhibit less overconfidence. The safety implications for a number of areas are discussed.

Collision Risk Assessment Support System for MASS RO and VTSO Support in Multi-Ship Environment of Vessel Traffic Service Area

The discussions by the International Maritime Organization for the introduction of a maritime autonomous surface ship (MASS) began in earnest. At the 27th ENAV meeting, the International Association of Marine Aids to Navigation and Lighthouse Authorities proposed the “sharing of a common operating picture for situational awareness of the waterway within vessel traffic service (VTS) environment” when developing a system to support MASS operation. Marine accidents caused by collisions on waterways still account for a high percentage of ship accidents that occur at sea, and many studies have investigated the risk of collision between ships. Collision risk assessment was primarily conducted in ship domain-based safety areas. This study evaluates the collision risk using the ship domain derived by the VTS operator (VTSO) and proposes a real-time collision risk assessment support system to improve the situational awareness of VTSOs and MASS remote operators (MASS ROs) regarding near-collision situations occurring in local waters. To evaluate the validity of the proposed system, a risk analysis was performed on near-collision scenarios at Busan Port. The results show that the distance to the closest point of approach (CPA), time to the CPA, and inter-ship distance converged within 0.5 nautical miles, 10 min, and 3 nautical miles, respectively.

State of science: evolving perspectives on 'human error'

This paper reviews the key perspectives on human error and analyses the core theories and methods developed and applied over the last 60 years. These theories and methods have sought to improve our understanding of what human error is, and how and why it occurs, to facilitate the prediction of errors and use these insights to support safer work and societal systems. Yet, while this area of Ergonomics and Human Factors (EHF) has been influential and long-standing, the benefits of the 'human error approach' to understanding accidents and optimising system performance have been questioned. This state of science review analyses the construct of human error within EHF. It then discusses the key conceptual difficulties the construct faces in an era of systems EHF. Finally, a way forward is proposed to prompt further discussion within the EHF community. Practitioner statement This state-of-science review discusses the evolution of perspectives on human error as well as trends in the theories and methods applied to understand, prevent and mitigate error. It concludes that, although a useful contribution has been made, we must move beyond a focus on an individual error to systems failure to understand and optimise whole systems.

Quantitative Analysis on Risk Influencing Factors in the Jiangsu Segment of the Yangtze River

Quantitative risk influencing factors (RIFs) are proposed, using the Conjugate Bayesian update approach to analyze 945 collision accidents and incidents cases from the Jiangsu Segment of the Yangtze River over five years from 2012 to 2016. The accident probability is compared under a pairwise comparison mode in order to reflect the relative risk between accidental situations. The Bayesian update mode is constructed to quantitatively evaluate the relative importance of different RIFs. The riskiest segment of Jiangsu Waterways as well the main causations of collisions are identified based on the distributions of collision risk in the six segments of the waterways. The results can support managers to develop the most effective policies to mitigate the collision risk.

A systematic approach to public health - Novel application of the human factors analysis and classification system to public health and COVID-19

In this article, we argue for a novel adaptation of the Human Factors Analysis and Classification System (HFACS) to proactive incidence prevention in the public health and in particular, during and in response to COVID-19. HFACS is a framework of causal categories of human errors typically applied for systematic retrospective incident analysis in high-risk domains. By leveraging this approach proactively, appropriate, and targeted measures can be quickly identified and established to mitigate potential errors at different levels within the public health system (from tertiary and secondary healthcare workers to primary public health officials, regulators, and policymakers).

Modelling of possible tanker accident oil spills in the Istanbul Strait in order to demonstrate the dispersion and toxic effects of oil pollution

Countries located on the Black Sea coast perform most of their sea trade through the Istanbul Strait (IS). Approximately 50,000 ships pass through the IS each year, with crude oil tankers making up the majority. Thus, the aim of the study is to determine the acute toxic effect of oil pollution that may occur as a result of crude oil tanker accidents in the IS. By utilising data related to accidents that have occurred in the IS, locations of concentrated tanker accidents, or "hot spots," were determined by Kernel Density Analysis. Subsequently, the distribution of potential leaks following an oil tanker accident, within these hot spots, is modelled with GNOME software. Finally, acute toxicity caused by oil pollution in the marine ecosystem is determined by Aliivibrio fischeri luminescent bacteria toxicity test. In this research, 5 hot spots are identified, where the maximum calculated amount of oil that can reach the coastline after 72 h is 3096 metric tons. Similarly, oil pollution can affect a total coastline of 30-35 km. Furthermore, it was determined that after the oil was diluted in seawater, at a ratio of 1:200,000, the toxic effects decrease (EC₅₀ above 100 mg/L), yet the chronic effects may still continue. The results of this study may serve as a reference for coastal state authorities to develop emergency response plans. Having this valuable knowledge of where high-risk accidents are most concentrated, where the accidents occur intensely, which areas can be affected by the pollution, the duration of the pollution effects, and the distance between the areas, will help determine the number of intervention stations to be installed, their locations, and equipment to be installed to the stations.

COLREGs Compliant Fuzzy-Based Collision Avoidance System for Multiple Ship Encounters

As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence, this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analysed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.

Review of Legal Aspects of Electrical Power Quality in Ship Systems in the Wake of the Novelisation and Implementation of IACS Rules and Requirement

This paper deals with new challenges regarding power quality in ship technology resulting from the novelisation and implementation of IACS (International Association of Classification Societies) rules and requirements. These rules, known as IACS E24 2016/2018, address harmonic distortion for ship electrical distribution systems, including harmonic filters. The reasons for the legislative changes based on a short overview of power quality-related accidents are discussed, after which a brief presentation of the updated IACS rules illustrated by a related DNV GL (Det Norske Veritas Germanischer Lloyd) case study is shown. A key part of this paper includes proposals concerning harmonics and interharmonics, distortion indices and transient disturbances. The aim of these proposals is to unify power quality indices and measurement procedures to maintain effective and comparable criteria for monitoring distortion and establish requirements for ship owners, designers, shipbuilders, classifiers, and crew members of marine objects.

Quantitative Ship Collision Frequency Estimation Models: A Review

Ship collisions are one of the most common types of maritime accidents. Assessing the frequency and probability of ship collisions is of great importance as it provides a cost-effective and practical way to mitigate risk. In this paper, we present a review of quantitative ship collision frequency estimation models for waterway risk assessment, accompanied by a classification of the models and a description of their main modelling characteristics. Models addressing the macroscopic perspective in the estimation of ship collision frequency on waterways are reviewed in this paper with a total of 29 models. We extend the existing classification methodology and group the collected models accordingly. Special attention is given to the criteria used to detect potential ship collision candidates, as well as to causation probability and the correlation of models with real ship collision statistics. Limitations of the existing models and future improvement possibilities are discussed. The paper can be used as a guide to understanding current achievements in this field.

Correlation in Causality: A Progressive Study of Hierarchical Relations within Human and Organizational Factors in Coal Mine Accidents

It has been revealed in numerous investigation reports that human and organizational factors (HOFs) are the fundamental causes of coal mine accidents. However, with various kinds of accident-causing factors in coal mines, the lack of systematic analysis of causality within specific HOFs could lead to defective accident precautions. Therefore, this study centered on the data-driven concept and selected 883 coal mine accident reports from 2011 to 2020 as the original data to discover the influencing paths of specific HOFs. First, 55 manifestations with the characteristics of the coal mine accidents were extracted by text segmentation. Second, according to their own attributes, all manifestations were mapped into the Human Factors Analysis and Classification System (HFACS), forming a modified HFACS-CM framework in China's coal-mining industry with 5 categories, 19 subcategories and 42 unsafe factors. Finally, the Apriori association algorithm was applied to discover the causal association rules among external influences, organizational influences, unsafe supervision, preconditions for unsafe acts and direct unsafe acts layer by layer, exposing four clear accident-causing "trajectories" in HAFCS-CM. This study contributes to the establishment of a systematic causation model for analyzing the causes of coal mine accidents and helps form corresponding risk prevention measures directly and objectively.

The Human Factor in Maritime Transport: Personality and Aggression Levels of Master Mariners and Navigation Students

The current study sought to identify groups of navigation students and master mariners (captains) characterized by different constellations of the Big Five personality traits and aggression levels. We hypothesized that master mariners would exhibit the resilient personality type and that navigation students would additionally exhibit personality types other than the resilient (e.g., over- or undercontrolled). A sample of 108 navigation students (men, in their second or third year of naval school, all active athletes) and 76 master mariners took part in the study and completed the Polish version of the NEO Five-Factor Inventory (Costa & McCrae, 1992; Zawadzki et al., 1998) and the Aggression Questionnaire (Buss & Perry, 1992; Tucholska, 1998). The hypothesis about the resilient personality type among master mariners was confirmed. The second hypothesis about personality types other than the resilient among navigation students was also confirmed: 33% of the students exhibited the overcontrolling personality type. The article also highlights the need to include psychological assessment in naval school enrolment procedures.

Identifying Cognitive Mechanism Underlying Situation Awareness of Pilots' Unsafe Behaviors Using Quantitative Modeling

Situation awareness (SA) of pilots’ unsafe behavior can ensure safety onboard. Thus, the cognitive mechanism that controls the SA leading to unsafe behavior must be articulated. This study employs the SA model and theory of planned behavior (TPB) to articulate a quantitative model of ship safe piloting. Firstly, the hierarchical classification framework of unsafe behaviors was constructed as an analytical foundation for rational and unconscious behaviors in sight of cognitive processes, and then the measurement elements of the cognitive mechanisms for behaviors were identified. Subsequently, based on the structural model, a hypothetical model of the cognitive path for unsafe behaviors was proposed by using the extended TPB, where there are four independent variables (i.e., attitude (ATD), subjective norm (SN), and perceived behavioral control (PBC)), one mediating variables (i.e., SA) and two dependent variables (i.e., behavioral intention (BI) and unsafe behaviors (BE)). Finally, this hypothetical model was analyzed with the data resources from extended TPB questionnaire of 295 pilots. Analysis results show that relationships of causation and mediation in the cognitive mechanism are in line with the behavior pattern and SA have a pronounced mediating effect and a strong relevance to the causal chain of extended TPB framework. This study integrated the SA three-level model to understand the motivation–cognition–action–feedback (MCAF) mechanism of pilots’ unsafe behaviors under cognitive mode of information processing through structural model. It would make a valuable contribution to the assessment and intervention of safety behaviors, and provide a basic framework for monitoring the situation awareness of pilot by man-machine interactive measurement technology in the future.

The Role of the Human Factor in Marine Accidents

A common interest of all shipping industry stakeholders is safe and accident-free shipping. To reach that goal, one of the most important actions that can be done is to analyze previous marine accidents. It means finding causes of accidents and, based on the analysis results, implementing effective corrective measures that can help reduce such undesired events in the future and improve safety efforts in shipping. Since it is widely accepted that human error accounts for 80–85% of all marine accidents, the research was focused on the human factor analysis in marine accidents. In this paper, 135 marine accident reports recorded in the UK Marine Accident Investigation Branch (MAIB) database from 2010 to 2019 were analyzed. The analysis aimed to categorize causal factors and discover the ones that are the most common. The Human Factor Analysis and Classification System for Maritime Accidents (HFACS-MA) method was used to be able to do so. Furthermore, multiple linear regression was used to determine the relationship between the number of accidents and the most common HFACS-MA causal factors. The research revealed that the causes of marine accidents are primarily dependent on two human factor categories and confirmed that by influencing those human factors categories, the number of marine accidents could be reduced and shipping safety improved in general.

Application of Measurement Sensors and Navigation Devices in Experimental Research of the Computer System for the Control of an Unmanned Ship Model

Unmanned autonomous transport vessels (MASS) are the future of maritime transport. The most important task in the design and construction of unmanned ships is to develop algorithms and a computer program for autonomous control. In order for such a computer program to properly control the ship (realizing various functions), the ship must be equipped with a computer system as well as measurement sensors and navigation devices, from which the recorded parameters are processed and used for autonomous control of the ship. Within the framework of conducted research on autonomous ships, an experimental model of an unmanned ship was built. This model was equipped with a propulsion system not commonly used on transport vessels (two azimuth stern thrusters and two bow tunnel thrusters), but providing excellent propulsion and steering characteristics. A complete computer system with the necessary measuring sensors and navigation devices has also been installed in the model of the ship, which enables it to perform all functions during autonomous control. The objective of the current research was to design and build a prototype computer system with the necessary measurement sensors and navigation devices with which to autonomously control the unmanned ship model. The designed computer system is expected to be optimal for planned tasks during control software tests. Tests carried out on open waters confirmed the correctness of the operation of the computer system and the entire measurement and navigation equipment of the built model of the unmanned transport vessel.

The Role of the Prefrontal Cortex and Functional Connectivity during Maritime Operations: An fNIRS study

INTRODUCTION

Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision-making can increase the likelihood of a collision.

METHODS

A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20-min period of sustained attention to locate a target vessel and (2) a 10-min period of decision-making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15-channel functional near-infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph-theoretic measures.

RESULTS

Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision-making. The graph-theoretic analysis revealed that density declined during decision-making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver.

CONCLUSIONS

The right lateral area of the PFC is sensitive to watchkeeping and decision-making during operational performance. Graph-theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety-critical performance.

Augmented Reality Lights for Compromised Visibility Navigation

This paper considers the feasibility of using augmented reality (AR) as a tool for enhancing visualization in maritime operations to avoid collision in different environmental conditions. According to the International Maritime Organization (IMO 2010), 90% of maritime accidents due to collisions at sea are caused in part by human error. This study investigates the new technology (AR) used to superimpose holographic images onto the real world; now reaching a state of readiness for commercial application. This paper demonstrates the competence of AR technology to serve as a maritime navigational aid. The research explores the viability of improving navigational safety in low visibility by projecting holograms of real-world objects in the same geo-location as the real object to make them “visible”. The paper presents the logical deconstruction of the technical problems and identified solutions, together with results of experiments used to validate the concept and technology readiness for real word maritime application. The paper presents a verified demonstrator; a proposed holographic bridge interface with an innovative way of presenting information using AR technology. Furthermore, it identifies that new technologies offer the opportunity for enhanced operator performances, with the expectation being that this should lead to reduce risk to persons, property, and the environment.

Nautical Collision Avoidance : The Cognitive Challenges of Balancing Safety, Efficiency, and Procedures

OBJECTIVE

Experimentally investigate maneuver decision preferences in navigating ships to avoid a collision. How is safety (collision avoidance) balanced against efficiency (deviation from path and delay) and rules of the road under conditions of both trajectory certainty and uncertainty.

BACKGROUND

Human decision error is a prominent factor in nautical collisions, but the multiple factors of geometry of collisions and role of uncertainty have been little studied in empirical human factors literature.

APPROACH

Eighty-seven Mechanical Turk participants performed in a lower fidelity ship control simulation, depicting ownship and a cargo ship hazard on collision or near-collision trajectories of various conflict geometries, while controlling heading and speed with the sluggish relative dynamics. Experiment 1 involved the hazard on a straight trajectory. In Experiment 2, the hazard could turn on unpredictable trials. Participants were rewarded for efficiency and penalized for collisions or close passes.

RESULTS

Participants made few collisions, but did so more often when on a collision path. They sometimes violated the instructed rules of the road by maneuvering in front of the hazard ship's path. They preferred speed control to heading control. Performance degraded in conditions of uncertainty.

CONCLUSION

Data reveal an understanding of maneuver decisions and conditions that affect the balance between safety and efficiency.

APPLICATION

The simulation and data highlight the degrading role of uncertainty and provide a foundation upon which more complex questions can be asked, asked of more trained navigators, and decision support tools examined.

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.

Analysis of Human Factors Relationship in Hazardous Chemical Storage Accidents

Human factors are important causes of hazardous chemical storage accidents, and clarifying the relationship between human factors can help to identify the logical chain between unsafe behaviors and influential factors in accidents. Therefore, the human factor relationship of hazardous chemical storage accidents was studied in this paper. First, the human factors analysis and classification system (HFACS), which originated from accident analysis in the aviation field, was introduced. Since some items were designed for aviation accident analysis, such as the item "Crew Resource Management", it is not fully applicable to the analysis of hazardous chemical storage accidents. Therefore, this article introduced some modifications and changes to make the HFACS model suitable for the analysis of hazardous chemical storage accidents. Based on the improved HFACS model, 42 hazardous chemicals storage accidents were analyzed, and the causes were classified. After analysis, we found that under the HFACS framework, the most frequent cause of accidents is resource management, followed by violations and inadequate supervision, and finally the organizational process and technological environment. Finally, according to the statistical results for the various causes of accidents obtained from the improved HFACS analysis, the chi-square test and odds ratio analysis were used to further explore the relevance of human factors in hazardous chemical storage accidents. The 16 groups of significant causal relationships among the four levels of factors include resource management and inadequate supervision, planned inappropriate operations and technological environment, inadequate supervision and physical/mental limitations, and technological environment and skill-based errors, among others.

Multi-Ship Collision Avoidance Decision-Making Based on Collision Risk Index

Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

An integrated offshore oil spill response decision making approach by human factor analysis and fuzzy preference evaluation

Human factors/errors (such as inappropriate actions by operators and unsafe supervision by organizations) are a primary cause of oil spill incidents. To investigate the influences of active operational failures and unsafe latent factors in offshore oil spill accidents, an integrated human factor analysis and decision support process has been developed. The system is comprised of a Human Factors Analysis and Classification System (HFACS) framework to qualitatively evaluate the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response); coupled with quantitative data analysis by Fuzzy Set Theory and the Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) to enhance decision making during response operations. The efficiency of the integrated human factor analysis and decision support system is tested with data from a case study to generate a comprehensive priority rank, a robust sensitivity analysis, and other theoretical/practical insights. The proposed approach improves our knowledge on the significance of human factors/errors on oil spill accidents and response operations; and provides an improved support tool for decision making.

Analysis of a catastrophic commercial coach crash based on an improved accident causation model: cause classification and lessons learned

Introduction. This study aimed to perform an in-depth analysis of a catastrophic coach crash that occurred on the Chinese expressway, and thus draw useful lessons to avoid similar mistakes. Methods. Various causes were identified from the investigation report based on a proposed accident causation model, which provides a universal pathway for accident analysis from the individual level to the organizational level. Results. Driver error in an emergency affected by fatigue and speeding led directly to the crash. Accordingly, drivers in coaches should be monitored effectively and their unsafe acts must be corrected in a timely manner to avoid the formation of unsafe habits. Based on individual flaws, weaknesses in the construction of the organizational safety management system and safety culture were further deduced and discussed. Conclusion. The work and rest system, as well as the dynamic monitoring system for drivers, should be perfected strictly according to the regulations. Additionally, external factors regarding deficiencies in the design and management of the expressway and the supervision of the transportation company also had great impact on this crash. In summary, more efforts should be taken regarding root causes at the organizational level, regardless of internal or external factors.

Can Survey Measures Predict Key Performance Indicators of Safety? Confirmatory and Exploratory Analyses of the Association Between Self-Report and Safety Outcomes in the Maritime Industry

Safety management may be improved if managers implement measures based on reliable empirical knowledge about how psychological factors cause or prevent accidents. While such factors are often investigated with self-report measures, few studies in the maritime industry have investigated whether self-report measures predict objectively registered accidents. The current pre-registered study used structural equation modelling to test whether “Safety attitude,” “Situation awareness,” “Reporting attitude” and “Safe behaviour” predicted “Number of reports” and “Number of safety events” in the following year. The study was conducted among crew on chemical tanker vessels operating in Arctic and Baltic waters. The pre-registered model of expected associations between self-reported safety factors and recorded safety outcomes was not supported. However, an exploratory model based on the pre-registered hypotheses supported an association between self-reported “Safe behaviour” and the overall number of recorded safety outcomes. While much safety research in the maritime industry builds on the assumption that self-reported behaviour, attitude or cognitions are causally related to actual accidents, the current study shows that such a relationship can be difficult to confirm. Until more conclusive studies are performed, the assumed causal relationship between self-reported psychological factors and safety outcomes should be treated with caution.

Exploring the Underlying Causes of Chinese Eastern Star, Korean Sewol, and Thai Phoenix Ferry Accidents by Employing the HFACS-MA

Maritime safety is a significant topic in the maritime industry since the numerous dangers at sea could lead to loss of property, environmental pollution, and even casualties. Existing research illustrates that human factors are the primary reasons of maritime accidents. Indeed, numerous maritime accidents can be classified into different types of human factors. In this context, the Human Factors Analysis and Classification System for Maritime Accidents (HFACS-MA) model is introduced in this paper. The HFACS-MA framework consists of five levels, complying with the core concepts of HFACS and the guiding principles of the International Maritime Organization (IMO). Based on the five levels of the framework, this research explores the underlying causes of Chinese Eastern Star, Korean Sewol, and Thai Phoenix accidents, and a comparative analysis is conducted. The analysis demonstrates the utility of applying the HFACS-MA model to the maritime industry, and the results emphasize the importance of the following categories: legislation gaps, organizational process, inadequate supervision, communication (ships and VTS), decision errors, and so on. Consequently, the research enables increased support for HFACS-MA and its application and provides valuable information for safety management and policy development in the maritime industry at different levels.

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.

Investigation and Analysis of a Hazardous Chemical Accident in the Process Industry: Triggers, Roots, and Lessons Learned

This paper performs an in-depth investigation and analysis on a catastrophic hazardous chemical accident involving domino effects in China based on an emerging accident causation model—the 24Model. The triggers and roots of the incident from the individual and organizational levels have been identified and several useful lessons have been summarized to avoid similar mistakes. This accident began with a leak of vinyl chloride caused by the failure of the gas holder’s bell housing and the operators’ mishandling. Leaked vinyl chloride was ignited by a high-temperature device in the process of diffusion and the fire quickly spread to the illegally parked vehicles. Several organizations were involved in this accident, and the chemical company should bear the main responsibility for it, and shall establish and implement an effective safety management system in its organizational structure and staffing, facilities management, hazards identification, emergency disposal, etc., to improve safety performance in a systematic way. Enterprises in the chemical industry park shall enhance the communication to clarify major hazard installations in their domains, and conduct regular safety evaluation for the plant as the external environment changed. Government agencies shall plan the layout of the chemical industry park scientifically and ensure safety starts with the design stage. The case study provides a practical procedure for accident investigation and analysis, and thus, preventive measures can be made according to the various causations at different levels.