Analysis of human errors in maritime accidents: A Bayesian spatial multinomial logistic model

Sheng, Tong Li; Weng, Jinxian; Shi, Kun; Han, Bing

doi:10.1080/19439962.2023.2235323

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…This refinement reflects the unique circumstances of fishermen, who often operate at sea with minimal crew and contend with harsh marine conditions, unlike on land. According to a study by Sheng et al [27], when the contributing factors to human error were investigated using the BSMNL model, season, visibility, and time of day could affect the probability of human error occurring. Therefore, we decided that adding environmental conditions as a new CPC would be effective in calculating human error in the fishing industry.…”

Section: Cream (Cognitive Reliability and Error Analysis Method)mentioning

confidence: 99%

“…HRA has also found numerous applications within the maritime sector. Sheng et al [27] proposed a Bayesian spatial multinomial logistic model (BSMNL) using geographic information obtained from historical maritime accidents. The proposed BSMNL model can be used to investigate the determinants of human errors associated with maritime accidents.…”

Section: Introductionmentioning

confidence: 99%

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Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)

Lee,

Kim,

Koo

et al. 2024

Sustainability

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In this paper, we introduce a model designed to predict human error probability (HEP) in the context of fishing boat operations utilizing the cognitive reliability and error analysis method (CREAM). We conducted an analysis of potential accidents on fishing boats and calculated the cognitive failure probability (CFP) for each identified accident. The common performance conditions (CPCs) from the original CREAM were adapted to better reflect the conditions on fishing boats, with the adapted CPCs’ validity confirmed through expert consultations. To apply CREAM, data were gathered via a survey of fishermen, with the uncertainty in the collected data addressed through the application of fuzzy set theory (FST). We then established a Bayesian network (BN) model to elucidate the relationship between the fuzzy data and HEP, utilizing a weighted sum algorithm to determine conditional probabilities within the BN. Both basic and extended versions of CREAM were applied to analyze the most common accidents among fishermen, calculating the CFP for each type of accident. According to our analysis, the poorer the dynamic CPC, the higher the probability that a fall accident will occur inside the boat due to human error, necessitating a countermeasure. The paper proposes safety enhancements for small fishing boats and illustrates the increased precision of human reliability analysis (HRA) models in forecasting human error by incorporating quantitative methods. It calls for further data collection and refinement of the model for more accurate operational risk assessments.

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Section: Cream (Cognitive Reliability and Error Analysis Method)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)

Lee,

Kim,

Koo

et al. 2024

Sustainability

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“…Consequently, Table 4 presents a selected sample of 29 high-impact articles categorized into three topics: 'maritime accidents'; 'risks of small fishing vessels', which explores the significance and risks associated with small fishing vessels; and 'vessel safety and environmental impact', focusing on the environmental consequences of fishing vessel accidents. 2 "A safety risk assessment for ship boarding parties from fuzzy Bayesian networks perspective" [40] 3 "An analysis of severity of oil spill caused by vessel accidents" [41] 4 "Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents" [42] 5 Risks of small fishing vessels "An operational risk awareness tool for small fishing vessels operating in harsh environment" [43] 6 "Sickness Absence and Hospitalization among Workers on Board Norwegian Fishing Vessels" [44] 7 "Risk perception in small-scale fishers and hyperbaric personnel: A risk assessment of hookah diving" [45] 8 "Influence of ship dynamics modelling on the prediction of fishing vessels roll response in beam and longitudinal waves" [46] 9 "Fishing for revenue: How leasing quota can be hazardous to your health" [47] 10 "Occupational health outcomes for workers in the agriculture, forestry and fishing sector: Implications for immigrant workers in the southeastern US" [48] 11 "Safety culture aboard fishing vessels" [49] 12 "Fishing safety policy and research" [50] 13 "Assessing the role of information and communication technologies in responding to 'slavery scandals'" [51] 21 "Towards safe navigation environment: The imminent role of spatio-temporal pattern mining in maritime piracy incidents analysis" [59] 22 "Noise sources and hazardous noise levels on fishing vessels: The case of Newfoundland and Labrador's fleet" [60] 23 "Exploring shipping accident contributory factors using association rules" [61] 24 "Wind-borne illness from coastal seas: Present and future consequences of toxic marine aerosols" [62] 25 "Health in fishing communities: A global perspective" [63] 26 "Aquatic food security: insights into challenges and solutions from an analysis of interactions between fisheries, aquaculture, food safety, human health, fish and human welfare, economy and environment" [64] 27 "A root cause analysis for Arctic Marine accidents from 1993 to 2011" [65] 28 "Analysis of human errors in maritime accidents: A Bayesian spatial multinomial logistic model" [66] 29 "Pattern investigation of total loss maritime accidents based on association...…”

Section: Bibliometric Analysis Of the Safety Aspects Of Small Fishing...mentioning

confidence: 99%

Investigating Fishing Vessel Casualties in Peru: A Technical and Scientific Review to Support New Regulations

Mantari,

Ramirez,

Figueroa

et al. 2023

JMSE

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The reporting system for fishing vessel and crew casualties in Peru needs a disruptive change in order to know what the risks in terms of marine safety are, who should manage them, and how they should be managed. According to a technical review on accidents recorded by the Peruvian Direction of Captaincies and Coast Guards (DICAPI), it is evident there is a need to process the available information on casualties of fishing vessels and crew in the fishing industry since this information is not easy to analyze. Nevertheless, this paper provides the casualties reported between 2004 and 2010, and from 2013 to 2020. In order to understand the research endeavors and trends surrounding the safety of fishing vessels, a systematic review using VOSviewer was performed. After a deep analysis and study of the information available in Peru, as well as reports and regulations from the International Maritime Organization (IMO), Food and Agriculture Organization (FAO), International Convention for the Safety of Life at Sea (SOLAS), and the recent advances available in the scientific literature on the safety of fishing vessels and crew casualties around the world, this paper presents some proposals regarding the current Peruvian scenario that are expected to be considered in new regulations.

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“…From the perspective of maritime safety, introducing autonomous ships offers the most noteworthy advantage of reducing maritime accidents caused by human factors. These factors account for the considerable influential proportion of maritime accidents [26][27][28][29][30]. However, new forms of human error, such as negligent management of onshore remote operators, may arise when humans intervene in any case classified by the IMO as level 1 to level 3.…”

mentioning

confidence: 99%

Systematization of Legal Procedures for Collision Avoidance between a Fully Autonomous Ship and a Traditional Manned Ship

Kim

2023

JMSE

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Discussions of autonomous ships are actively being conducted in the industry and by the International Maritime Organization (IMO). In addition, it is anticipated that a significant number of autonomous ships will be operational at sea soon, as a trial run of autonomous ships is underway. Fully autonomous ships will operate based on pre-programmed algorithms to prevent collisions, eliminating the need for onboard navigators or remote operators onshore. Most collision avoidance algorithms are typically based on an engineering approach that predicts the future movement of an approaching ship by observing its vector. However, it is worth noting that even if fully autonomous ships navigate at sea, the majority of ships encountered are still operated by humans. These ships adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG). Therefore, even fully autonomous ships can effectively and legally avoid approaching ships only when they are steered in compliance with the COLREG. However, it has rarely been addressed which procedures should be followed to determine the legally correct action in various situations where fully autonomous ships encounter traditional manned ships. Therefore, this study is divided into two parts. First, a decision-making tree is presented, as simply as possible, to determine the legally correct collision avoidance action according to the COLREG. Secondly, a quantitative analysis is presented for qualitative expressions such as “narrow channel”, “restricted visibility”, and “best aid to avoid collision”. This review will help fully autonomous ships determine legitimate collision avoidance actions and operate safely in seas where human-operated ships are sailing. However, for autonomous ships, the “Trolley problem” and issues related to decision-making for collision avoidance through communication with other ships are left as future challenges.

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Analysis of human errors in maritime accidents: A Bayesian spatial multinomial logistic model

Cited by 8 publications

References 36 publications

Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)

Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)

Investigating Fishing Vessel Casualties in Peru: A Technical and Scientific Review to Support New Regulations

Systematization of Legal Procedures for Collision Avoidance between a Fully Autonomous Ship and a Traditional Manned Ship

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