Determination of a safety criterion via risk assessment of marine accidents based on a Markov model with five states and MCMC simulation and on three risk factors

Jon, Min Hyok; Kim, Yun Pyong; Choe, Uk

doi:10.1016/j.oceaneng.2021.109000

Cited by 21 publications

(6 citation statements)

References 32 publications

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“…( 8) Extensibility-the ability to handle large-scale parameters. [93] Simulation [102] CN [49] FMEA [99] FTA [93] Game theory [103] Machine learning [98] FRAM [89] HFACS [94] STPA [104] Markov model [44] DBN [44]…”

Section: Overview Of the Research Methodsmentioning

confidence: 99%

A Systematic Literature Review of Maritime Transportation Safety Management

Xu,

Ma,

Zhao

et al. 2023

JMSE

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Maritime transportation plays a critical role in global trade, and studies on maritime transportation safety management are of great significance to the sustainable development of the maritime industry. Consequently, there has been an increasing trend recently in studies on maritime transportation safety management, especially in terms of safety risk analysis and emergency management. Therefore, the general idea of this article is to provide a detailed literature review of maritime transportation safety management based on 186 articles in the Web of Science (WOS) database published from 2011 to 2022. The purposes of this article are as follows: (1) to provide a statistics-based description and conduct a network-based bibliometric analysis on the basis of the collected articles; (2) to summarize the methodologies/technologies employed in maritime transportation safety management spatiotemporally; and (3) to propose four potential research perspectives in terms of maritime transportation safety management. Based on the findings and insights obtained from the bibliometric and systematic review, the development of a resilient maritime transportation system could be facilitated by means of data- or intelligence-driven technologies, such as scenario representation, digital twinning, and data simulation. In addition, the issues facing intelligent maritime shipping greatly challenge the current maritime safety management system due to the co-existence of intelligent and non-intelligent maritime operation.

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Section: Overview Of the Research Methodsmentioning

confidence: 99%

A Systematic Literature Review of Maritime Transportation Safety Management

Xu,

Ma,

Zhao

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Zhang and Meng (2019) proposed a probabilistic ship domain with a vague domain boundary rather than a crisp value with the desired boundary. Jon et al (2021) proposed a safety criterion based on the risk assessment of marine accidents. The assessment was done by combining a Markov model and a Markov chain Monte Carlo simulation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Jon et al. (2021) proposed a safety criterion based on the risk assessment of marine accidents. The assessment was done by combining a Markov model and a Markov chain Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

A framework of marine collision risk identification strategy using AIS data

Ma,

Shi,

Shi

et al. 2023

J. Navigation

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Collisions are one of the major accidents in the shipping industry, causing significant losses. In this work, a framework of marine collision risk identification strategy was developed to quantitatively analyse collision risks and provide an easy and convenient way to monitor traffic flow in relevant waters to mitigate the chances of collision. The model was verified by using automatic identification system data obtained from Tianjin Port. When compared to previous research, the proposed model can identify risks earlier and give people more time to analyse and take action. The results indicate that it also can provide a visual display to alert relevant personnel. The model can be used as a reference to identify potential collision risks or as an information source for future research.

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“…From the following articles, it can be seen that MC has been used in maritime research, whether in the study of traffic density and flows, ship collisions and groundings, pollution, or ship maintenance [30][31][32][33]. An interesting model that combines the Markov model and MC to estimate the marine accident risks is presented by Jon et al [34]. The advantage of this method is its simplicity, its flexibility in application to any type of maritime accidents, and the fact that the method does not require extensive data collection.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Ship Collision Probability Model Based on Monte Carlo Simulation and Bi-LSTM

Vukša

Vidan

Bukljaš

et al. 2022

JMSE

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The efficiency and safety of maritime traffic in a given area can be measured by analyzing traffic density and ship collision probability. Maritime traffic density is the number of ships passing through a given area in a given period of time. It can be measured using vessel tracking systems, such as the Automatic Identification System (AIS). The information provided by AIS is real-time data designed to improve maritime safety. However, the AIS data can also be used for scientific research purposes to improve maritime safety by developing predictive models for collisions in a research area. This article proposes a ship collision probability estimation model based on Monte Carlo simulation (MC) and bidirectional long short-term memory neural network (Bi-LSTM) for the maritime region of Split. The proposed model includes the processing of AIS data, the verification of AIS data, the determination of ports and ship routes, MC and the collision probability, the Bi-LSTM learning process based on MC, the ship collision probability for new or existing routes, and the traffic density. The results of MC, i.e., traffic/vessel route and density, and collision probability for the study area can be used for Bi-LSTM training with the aim of estimating ship collision probability. This article presents the first part of research that includes MC in detail, followed by a preliminary result based on one day of processed AIS data used to simulate MC and propose a model architecture that implements Bi-LSTM for ship collision probability estimation.

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Determination of a safety criterion via risk assessment of marine accidents based on a Markov model with five states and MCMC simulation and on three risk factors

Cited by 21 publications

References 32 publications

A Systematic Literature Review of Maritime Transportation Safety Management

A Systematic Literature Review of Maritime Transportation Safety Management

A framework of marine collision risk identification strategy using AIS data

Research on Ship Collision Probability Model Based on Monte Carlo Simulation and Bi-LSTM

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