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

The human element in future Maritime Operations - perceived impact of autonomous shipping

The role of the human element within complex socio-technical systems is continually being transformed and redefined by technological advancement. Autonomous operations across varying transport domains are in differing stages of realisation and practical implementation, and specifically within maritime operations, is still in its infancy. This study explores the potential effects of autonomous technologies on future work organisation and roles of humans within maritime operations. Ten Subject-Matter Experts working within industry and academia were interviewed to elicit their perspectives on the current state and future implications of autonomous technologies. Four main themes emerged: (i) Trust, (ii) Awareness and Understanding, (iii) Control, (iv) Training and Organisation of Work. A fuzzier fifth theme also appeared in the data analysis: (v) Practical Implementation Considerations, which encompassed various sub-topics related to real-world implementation of autonomous ships. The results provide a framework of human element issues relevant for the organisation and implementation of autonomous maritime operations. Practitioner summary: As autonomous shipping rapidly moves closer to real-world implementation, it is critical to develop an understanding of future roles of humans in autonomous maritime operations. By eliciting expert knowledge from academics and practitioners, we establish a framework of relevant issues facing humans in emerging autonomous systems and operations at sea.

Dynamics Simulation of the Risk Coupling Effect between Maritime Pilotage Human Factors under the HFACS Framework

Maritime pilotage is an important guarantee for the safety of water traffic in port. The pilot is affected by the complex port environment, the differences of crew and equipment of different ships, the physical and psychological pressure of the pilot himself, as well as the management factors from the pilot station and maritime safety administration. In order to avoid pilotage accidents (PAs), it is necessary to study the coupling effect of human-organizational factors (HOFs) on PAs. In this paper, from the perspective of HOF risk coupling in pilotage, the problem of HOF risk coupling in maritime pilotage is studied by using the hierarchical classification idea of the human factors analysis and classification system (HFACS) and the method of system dynamics (SD). First of all, HFACS is used to analyse the HOF risk causal elements (RCEs) in pilotage, and 70 RCEs are summed up in four layers; secondly, the SD coupling model of RCEs is constructed; finally, based on a dataset of PAs collected by the Shanghai Harbour Pilot Association, the coupling simulation of RCEs in pilotage is carried out, and the volatility is evaluated. In general, the safety situation of maritime pilotage has been improving in the Shanghai port. However, four RCEs (negligence, habit, pilotage experience, and violations) in unsafe acts and two RCEs (teamwork and personal safety awareness) in precondition for unsafe acts contribute the most to maritime PAs and need to be paid attention to.

Time-Varying Risk Measurement for Ship Collision Prevention

We propose an innovative time-varying collision risk (TCR) measurement for ship collision prevention in this article. The proposed measurement considers the level of danger of the approaching ships and the capability of a ship to prevent collisions. We define the TCR as the probability of the overlap of ships' positions in the future, given the uncertainty of maneuvers. Two sets are identified: (1) the velocity obstacle set as the maneuvers of the own ship that lead to collisions with target ships, and (2) the reachable velocity set as the maneuvers that the own ship can reach regarding its maneuverability. We then measure the TCR as the time-dependent percentage of overlap between these two sets. Several scenarios are presented to illustrate how the proposed measurement identifies the time-varying risk levels, and how the approach can be used as an intuitively understandable tool for collision avoidance.

AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model

Air traffic control (ATC) performance is important to ensure flight safety and the sustainability of aviation growth. To better evaluate the performance of ATC, this paper introduces the HFACS-BN model (HFACS: Human factors analysis and classification system; BN: Bayesian network), which can be combined with the subjective information of relevant experts and the objective data of accident reports to obtain more accurate evaluation results. The human factors of ATC in this paper are derived from screening and analysis of 142 civil and general aviation accidents/incidents related to ATC human factors worldwide from 1980 to 2019, among which the most important 25 HFs are selected to construct the evaluation model. The authors designed and implemented a questionnaire survey based on the HFACS framework and collected valid data from 26 frontline air traffic controllers (ATCO) and experts related to ATC in 2019. Combining the responses with objective data, the noisy MAX model is used to calculate the conditional probability table. The results showed that, among the four levels of human factors, unsafe acts had the greatest influence on ATC Performance (79.4%), while preconditions for safe acts contributed the least (40.3%). The sensitivity analysis indicates the order of major human factors influencing the performance of ATC. Finally, this study contributes to the literature in terms of methodological development and expert empirical analysis, providing data support for human error management intervention of ATC in aviation safety.

Foundational Elements of a Rendezvous: When (and Where) Planning & Prediction Meet

For one entity to initiate a meeting, interception, or rendezvous with another, such situations necessitate both accurate perception of the target’s trajectory and also planning of one’s own trajectory. To examine these cognitive processes and resulting performance trends, the current study presented participants with simplified simulated ship rendezvous scenarios in which they had to predict a target ship’s future course while planning their own actions to facilitate a meeting. Rendezvous error increased under high time pressure, either when the ships started by moving towards each other, or when the target ship moved faster than the user’s ship. When participants missed a rendezvous, they often did so by passing behind the target ship as if maneuvering too late. Participants also preferred to adjust both the heading and the speed of their ship when maneuvering. Finally, users took longer to assess situations characterized by high time pressure.

Development of Navigator Behavior Models for the Evaluation of Collision Avoidance Behavior in the Collision-Prone Navigation Environment

In order to prevent ship collisions, it is important to understand the behavior of navigators that leads to these collisions. The main cause of marine accidents in the Republic of Korea is attributed to navigator error, particularly in collisions. Hence, reducing navigator error is a key issue that needs to be addressed to prevent accidents. However, the lack of objective measure to quantify navigator error remains a challenge. The purpose of this study is to develop an objective identification of a navigator’s behavior in a collision encountering situation. Two behavior models for the success and failure of collision avoidance are developed by collecting participants’ actions, using a ship maneuvering simulator within a given scenario. These maneuvering behavior models are validated in terms of their discrimination powers. The results show that maneuvering behavior is clearly identified in the data processing and model development phases. The proposed behavior models are expected to provide a better understanding of how navigators behave to help reduce collision accidents.

Relational Model of Accidents and Vessel Traffic Using AIS Data and GIS: A Case Study of the Western Port of Shenzhen City

Following the growth in global trade activities, vessel traffic has increased dramatically in some busy waterways and ports. However, such increments have made it more complex to manage the regional vessel traffic, which can increase the risk of an accident in the area. To model and analyze the relationship between vessel traffic and maritime traffic, this paper proposes a gridded geography information system (GIS)-based relation analysis model using the historical automatic identification system (AIS) data and accident records over a 10-year-span. Firstly, the extent of the hazards posed by a maritime accident in terms of hull loss, fatality, and direct economic loss is quantified using set pair analysis. Consequently, the hazardous degree posed by an accident is obtained. The relative consequence of the regional hazard (RCORH) is then estimated by summing up all the relative hazardous degrees of accidents that have occurred in a certain gridded area. Secondly, the vessel traffic in the gridded areas is analyzed using characteristics such as speed, heading variance, and traffic volume as indicators. Based on the analysis of both the maritime traffic accidents and the vessel traffic, the spatial relationships are analyzed with an overlay between the RCORH and vessel traffic data of each grid, as well as a regression analysis. In a case study of the Western port of Shenzhen City, China, the methodology proves to be effective for vessel traffic management and traffic engineering design.

Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

Many causal factors to marine traffic accidents (MTAs) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to accident mechanisms, the complex structural chains on causes to MTA systems were analyzed by combining the human failure analysis and classification system (HFACS) with theoretical structural equation modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of a MTA, and the constituent elements of the causes of the accident were conducted. Second, a hypothetical model of human factors classification was proposed by applying the practice of the structural model. Third, with the data resources from ship accident cases, this hypothetical model was discussed and simulated, and as a result, the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behavior. Application examples show that relationships in the HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

Risk management abilities in multimodal maritime supply chains: Visibility and control perspectives

Supply chain complexity and disintegration lead to increased uncertainty from a stakeholders' perspective, which is emerging as one of the major challenges of risk management. The ability to identify risks has weakened, as the responsibility of supply chain risk management is handed over to outside service providers. Regardless, the risks, their visibility and their impact depend on the position of the companies in the supply chain. The actors in the chain must therefore collaborate to create effective risk management conditions. This challenging situation is especially pronounced in multimodal maritime supply chains, where the risks and actor focality are high. This paper contributes to current risk management literature by providing a holistic and systemic view of risk visibility and control in maritime supply chains. The study employs broad-based, qualitative interview data collected from actors operating in southern Finland and the Gulf of Finland as well as an expert-panel assessment of the related risk management abilities. The results show a high level of variance in the level of risk identification and visibility between the actors in question. This further suggests that collaboration in supply chain risk management is essential, as an awareness of the risks and their control mechanisms do not necessarily reside in the same company.

Analysis of Lookout Activity in a Simulated Environment to Investigate Maritime Accidents Caused by Human Error

Maritime accidents remain a significant concern for the shipping industry, despite recent technological developments. In the Republic of Korea, the leading cause of maritime accidents is navigator error, particularly in collisions and groundings; this cause has led to 79% of maritime accidents, according to a recent assessment. The reduction of navigator error is crucial for accident prevention; however, the lack of objective measures to monitor navigator error remains a challenge. The purpose of this study was to develop an objective classification of navigation behaviors in a simulated environment. The statistical model of classification of lookout activity was developed by collecting participants’ lookout behavior using a Kinect sensor within a given scenario. This classification model was validated in non-scenario experiments. The results showed that seven standard lookout activities during a lookout routine were accurately classified in both the model development and validation phases. The proposed model classification of lookout activity using an optical sensor is expected to provide a better understanding of how navigators behave to help prevent maritime accidents in practice.

A Hybrid HFACS-BN Model for Analysis of Mongolian Aviation Professionals’ Awareness of Human Factors Related to Aviation Safety

Safety is crucial to ensure the sustainability of aviation growth. To better clarify the influences of human factors on aviation accident risks, this study developed a hybrid HFACS-BN model (HFACS: Human Factors Analysis and Classification System; BN: Bayesian Network). The authors designed and implemented a questionnaire survey based on the four-level HFACS framework and collected valid data from 180 out of 649 aviation professionals working in the Ulaanbaatar International Airport, Mongolian in 2017. The model identified 35 major human factors out of 129 factors. The model validation was performed in terms of content validity and predictive validity. The results showed that even though a majority of respondents perceived that many human factors had a middle- or high-effect on aviation accident risks, the probability of the risks caused by human factors was estimated to be just 1.37%. The Unsafe Acts level is most influential to the risks among the four levels, while the Unsafe Supervision level contributes least. It is revealed that enhancing aviation professionals’ awareness of human factors should make full use of causal chaining effects among human factors. Finally, this study contributes to the literature from the perspectives of both methodological development and important empirical analysis.

Pierre M, Weigand MA, Lichtenstern C. Applying the human factors analysis and classification system to critical incident reports in anaesthesiology

BACKGROUND

The Human Factors Analysis and Classification System (HFACS) was developed as a practical taxonomy to investigate and analyse the human contribution to accidents and incidents. Based on Reason's "Swiss Cheese Model", it considers individual, environmental, leadership and organizational contributing factors in four hierarchical levels. The aim of this study was to assess the applicability of a modified HFACS taxonomy to incident reports from a large, anonymous critical incident database with the goal of gaining valuable insight into underlying, more systemic conditions and recurring schemes that might add important information for future incident avoidance.

METHODS

We analysed 50 reports from an anonymous, anaesthesiologic, single-centre Critical Incident Reporting System using a modified HFACS-CIRS taxonomy. The 19 HFACS categories were further subdivided into a total of 117 nanocodes representing specific behaviours or preconditions for incident development.

RESULTS

On an individual level, the most frequent contributions were decision errors, attributed to inadequate risk assessment or critical-thinking failure. Communication and Coordination, mostly due to inadequate or ineffective communication, was contributory in two-thirds of reports. Half of the reports showed contributory complex interactions in a sociotechnical environment. Ratability scores were noticeably lower for categories evaluating leadership and organizational influences, necessitating careful interpretation.

CONCLUSIONS

We applied the HFACS taxonomy to the analysis of CIRS reports in anaesthesiology. This constitutes a structured approach that, especially when applied to a large data set, might help guide future mitigation and intervention strategies to reduce critical incidents and improve patient safety. Improved, more structured reporting templates could further optimize systematic analysis.

Analysis of "2·28" KEEPER Chemical Industries Hazardous Chemical Explosion Accident Based on FTA and HFACS

On 28 February 2012, a guanidine nitrate explosion occurred at HEBEI KEEPER Chemical Industries Co., Ltd., China, resulting in 25 deaths, with 4 missing individuals and 46 injured. In order to explore the causal relationship hidden behind this accident, fault tree analysis (FTA) and the Human Factors Analysis and Classification System (HFACS) were used to systematically analyze the incident. Firstly, FTA was used to analyze the causes of the accident in depth, until all the basic causal events causing the guanidine nitrate explosion were identified, and a fault tree diagram of the guanidine nitrate explosion was drawn. Secondly, for the unsafe acts in the basic causal events, the HFACS model was used to analyze the three levels of factors that lead to unsafe acts, including the preconditions for unsafe acts, unsafe supervision, and organizational influences. Finally, based on the analysis results of FTA and HFACS, a complete logic diagram of the causes of the accident was obtained. The FTA and HFACS accident analysis methods allowed for the identification of human factors and the accident evolution process in the explosion accident and provide a reference for accident investigation.

Wrong-way driving crashes: A multiple correspondence approach to identify contributing factors

OBJECTIVE

Wrong-way driving (WWD) crashes result in 1.34 fatalities per fatal crash, whereas for other non-WWD fatal crashes this number drops to 1.10. As such, further in-depth investigation of WWD crashes is necessary. The objective of this study is 2-fold: to identify the characteristics that best describe WWD crashes and to verify the factors associated with WWD occurrence.

METHODS

We collected and analyzed 15 years of crash data from the states of Illinois and Alabama. The final data set includes 398 WWD crashes. The rarity of WWD events and the consequently small sample size of the crash database significantly influence the application of conventional log-linear models in analyzing the data, because they use maximum-likelihood estimation. To overcome this issue, in this study, we employ multiple correspondence analysis (MCA) to define the structure of the crash data set and identify the significant contributing factors to WWD crashes on freeways.

RESULTS

The results of the present study specify various factors that characterize and influence the probability of WWD crashes and can thus lead to the development of several safety countermeasures and recommendations. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions were among the most significant contributors to WWD crashes.

CONCLUSIONS

Despite many other methods that identify only the contributing factors, this method can identify possible associations between various contributing factors. This is an inherent advantage of the MCA method, which can provide a major opportunity for state departments of transportation (DOTs) to select safety countermeasures that are associated with multiple safety benefits.

An Evidential Reasoning-Based CREAM to Human Reliability Analysis in Maritime Accident Process

This article proposes a modified cognitive reliability and error analysis method (CREAM) for estimating the human error probability in the maritime accident process on the basis of an evidential reasoning approach. This modified CREAM is developed to precisely quantify the linguistic variables of the common performance conditions and to overcome the problem of ignoring the uncertainty caused by incomplete information in the existing CREAM models. Moreover, this article views maritime accident development from the sequential perspective, where a scenario- and barrier-based framework is proposed to describe the maritime accident process. This evidential reasoning-based CREAM approach together with the proposed accident development framework are applied to human reliability analysis of a ship capsizing accident. It will facilitate subjective human reliability analysis in different engineering systems where uncertainty exists in practice.

Developing a contributing factor classification scheme for Rasmussen's AcciMap: Reliability and validity evaluation

One factor potentially limiting the uptake of Rasmussen's (1997) Accimap method by practitioners is the lack of a contributing factor classification scheme to guide accident analyses. This article evaluates the intra- and inter-rater reliability and criterion-referenced validity of a classification scheme developed to support the use of Accimap by led outdoor activity (LOA) practitioners. The classification scheme has two levels: the system level describes the actors, artefacts and activity context in terms of 14 codes; the descriptor level breaks the system level codes down into 107 specific contributing factors. The study involved 11 LOA practitioners using the scheme on two separate occasions to code a pre-determined list of contributing factors identified from four incident reports. Criterion-referenced validity was assessed by comparing the codes selected by LOA practitioners to those selected by the method creators. Mean intra-rater reliability scores at the system (M = 83.6%) and descriptor (M = 74%) levels were acceptable. Mean inter-rater reliability scores were not consistently acceptable for both coding attempts at the system level (M_T1 = 68.8%; M_T2 = 73.9%), and were poor at the descriptor level (M_T1 = 58.5%; M_T2 = 64.1%). Mean criterion referenced validity scores at the system level were acceptable (M_T1 = 73.9%; M_T2 = 75.3%). However, they were not consistently acceptable at the descriptor level (M_T1 = 67.6%; M_T2 = 70.8%). Overall, the results indicate that the classification scheme does not currently satisfy reliability and validity requirements, and that further work is required. The implications for the design and development of contributing factors classification schemes are discussed.

Rasmussen's legacy in the great outdoors: A new incident reporting and learning system for led outdoor activities

Jens Rasmussen's seminal risk management framework and accompanying Accimap method have become highly popular in safety science circles. Despite this, widespread adoption of the model and method in practice has not yet been achieved. This paper describes a project involving the development and implementation of an incident reporting and learning system underpinned by Rasmussen's risk management framework and Accimap method. The system was developed for the led outdoor activity sector in Australia to enable reporting and analysis of injuries and near miss incidents, with the aim of supporting the development of more effective countermeasures. An analysis of the data derived from the first 3 months use of the system by 43 organisations is presented. The outputs provide an in-depth Accimap-based analysis of all incidents reported by participating organisations over the 3 month period. In closing, the importance of developing usable domain specific tools to support translation of Ergonomics theory and methods in practice is discussed.

Fitting methods to paradigms: are ergonomics methods fit for systems thinking?

The issues being tackled within ergonomics problem spaces are shifting. Although existing paradigms appear relevant for modern day systems, it is worth questioning whether our methods are. This paper asks whether the complexities of systems thinking, a currently ubiquitous ergonomics paradigm, are outpacing the capabilities of our methodological toolkit. This is achieved through examining the contemporary ergonomics problem space and the extent to which ergonomics methods can meet the challenges posed. Specifically, five key areas within the ergonomics paradigm of systems thinking are focused on: normal performance as a cause of accidents, accident prediction, system migration, systems concepts and ergonomics in design. The methods available for pursuing each line of inquiry are discussed, along with their ability to respond to key requirements. In doing so, a series of new methodological requirements and capabilities are identified. It is argued that further methodological development is required to provide researchers and practitioners with appropriate tools to explore both contemporary and future problems. Practitioner Summary: Ergonomics methods are the cornerstone of our discipline. This paper examines whether our current methodological toolkit is fit for purpose given the changing nature of ergonomics problems. The findings provide key research and practice requirements for methodological development.

The Relationship Between Job Stress, Job Satisfaction, and the Symptom Checklist-90-Revision (SCL-90-R) in Marine Officers on Board

Objectives

This study was conducted to investigate the relationships among job stress, job satisfaction, and mental health in marine officers.

Methods

The researchers gathered data on marine officers working at a harbor in Chungcheong Province, South Korea, using a self-reported questionnaire. Mental health was measured by the Symptom Checklist-90-Revision (SCL-90-R), and general characteristics including socioeconomic factors, job stress, and job satisfaction were measured by structured questionnaires. Multiple regression analysis was performed to investigate the relationships among job stress, job satisfaction, and mental health status according to the symptom dimensions of the SCL-90-R.

Results

Among the marine officers, obsessive-compulsive behavior, depression, and somatization were the most problematic symptoms. Those who reported poor health, low job satisfaction, and high job stress had a higher prevalence of psychoticism, somatization, depression, anxiety, and phobic anxiety.

Conclusions

An occupational health system should be introduced that would regularly check the mental health of marine officers in charge of ships and sailors, in order to help reduce their stress levels, enhance their job satisfaction, and thereby improve their mental health.

[Patient safety in anesthesiology : Multimodal strategies for perioperative care]

The concept of patient safety is more of a presence in modern day medicine than ever before. All measures and processes aiming at preempting and preventing critical incidents and accidents in patient care are collectively grouped under this concept. With the Declaration of Helsinki in 2010 modern anesthesiology has undertaken a fundamental role in the clinical implementation and perioperative clinicians are confronted with a multitude of theories, models, methods and tools targeted at improving or facilitating optimized patient safety. One of the biggest challenges lies in their synergistic combination to create a functioning concept for perioperative risk management while also incorporating individual strengths and weaknesses. The aim of the present paper is to provide a structured overview of the various components presently available for increasing perioperative patient safety.