Searching for the origins of the myth: 80% human error impact on maritime safety

Wróbel, Krzysztof

doi:10.1016/j.ress.2021.107942

Cited by 60 publications

(15 citation statements)

References 74 publications

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“…Rather, the analysis of the keywords suggests that it was studied along with more technical or general issues. Even though humans are often blamed for causing accidents [70], [71], research on their actual performance is rather scarce [72]. Meanwhile, outside the maritime domain, SA was more often studied in relation to issues concerning the human operator, such as human-computer interaction, situation prediction, and scene perception [73], [74], see Tables II & III.…”

Section: Research Gaps In Maritime Domainmentioning

confidence: 99%

“…The observed disparity of keywords between the considered transportation modes may arise from at least the following: (1) a different emphasis on the research priorities as determined by stakeholders or (2) intrinsic differences in the operational aspects of particular transportation modes. For instance, it is postulated that a large portion of maritime accidents involves human-centered causal factors [71], [77], but human-oriented aspects of SA are not as commonly investigated as in others transport modes.…”

Section: Research Gaps In Maritime Domainmentioning

confidence: 99%

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A Cross-Domain Scientometric Analysis of Situational Awareness of Autonomous Vehicles With Focus on the Maritime Domain

Bogusllwski

Nasur

et al. 2022

IEEE Access

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Highly automated vehicles are making their way towards implementation in many modes of transportation, including shipping. From the safety perspective, it is critically important that such vehicles or the operators overseeing them maintain their sense of the environment, also referred to as situational awareness. The present study investigates the worldwide research effort focusing on situational awareness for autonomous transport and explores how the maritime domain could benefit from it. The results indicate that most of the research originates from the automotive, but the topic is developing fast in other transportation modes too. Some findings have been shared across the modes of transportation, but only to a limited extent. Although technology development is performed based on the achievements within basic research domains, there has been little feedback from applied sciences. Similarly, collaborative research is not strongly developed.

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Section: Research Gaps In Maritime Domainmentioning

confidence: 99%

Section: Research Gaps In Maritime Domainmentioning

confidence: 99%

A Cross-Domain Scientometric Analysis of Situational Awareness of Autonomous Vehicles With Focus on the Maritime Domain

Bogusllwski

Nasur

et al. 2022

IEEE Access

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“…The ship susceptibility to an accident is conventionally assessed, either in the literature or in practice, with the use of various types of proximity indicators, such as distance and time to the closest point of approach (DCPA, TCPA) or ship domains [55]. However, none of these account for the actual mechanism that governs and quantifies the accident occurrence through the human reliability assessment, which is claimed to be the major driving factor for the accidents [56][57][58][59].…”

Section: Introductionmentioning

confidence: 99%

A framework for onboard assessment and monitoring of flooding risk due to open watertight doors for passenger ships

Ruponen¹,

Montewka²,

Tompuri³

et al. 2022

Reliability Engineering & System Safety

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“…Maritime transportation has long been a vital mode of international trade, accounting for more than 80% of worldwide exchanges. The continuous increase in maritime traffic and flows has led to critical stakeholder navigation monitoring and security challenges in many overcrowded maritime areas [ 1 ]. This entails the need for data mining approaches that can provide decision-aided solutions for extracting unusual and dangerous ship behaviors.…”

Section: Introductionmentioning

confidence: 99%

A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns

Suo

Zhang

et al. 2022

Sensors

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The successful emergence of real-time positioning systems in the maritime domain has favored the development of data infrastructures that provide valuable monitoring and decision-aided systems. However, there is still a need for the development of data mining approaches oriented to the detection of specific patterns such as unusual ship behaviors and collision risks. This research introduces a CSBP (complex ship behavioral pattern) mining model aiming at the detection of ship patterns. The modeling approach first integrates ship trajectories from automatic identification system (AIS) historical data, then categorizes different vessels’ navigation behaviors, and introduces a visual-oriented framework to characterize and highlight such patterns. The potential of the model is illustrated by a case study applied to the Jiangsu and Zhejiang waters in China. The results show that the CSBP mining model can highlight complex ships’ behavioral patterns over long periods, thus providing a valuable environment for supporting ship traffic management and preventing maritime accidents.

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Searching for the origins of the myth: 80% human error impact on maritime safety

Cited by 60 publications

References 74 publications

A Cross-Domain Scientometric Analysis of Situational Awareness of Autonomous Vehicles With Focus on the Maritime Domain

A Cross-Domain Scientometric Analysis of Situational Awareness of Autonomous Vehicles With Focus on the Maritime Domain

A framework for onboard assessment and monitoring of flooding risk due to open watertight doors for passenger ships

A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns

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