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Maritime Transportation Safety and Risk Management
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Maritime Transportation Safety and Risk Management

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Coastal Engineering".

Deadline for manuscript submissions: 1 June 2026 | Viewed by 3945

Special Issue Editors

Dr. Francis Obeng

E-Mail Website
Guest Editor
Fisheries and Marine Institute of Memorial University of Newfoundland, St John's, NL, Canada
Interests: maritime human factors; risk-informed decision analysis under uncertainty; AI integration in maritime safety and risk management; ship and offshore structures; fatigue; FEM/FEA
Dr. Rob Brown

E-Mail Website
Guest Editor
Fisheries and Marine Institute of Memorial University of Newfoundland, St. John’s, NL, Canada
Interests: numerical modelling of systems and human behaviour; maritime safety; human factors; maritime and arctic evacuation, survival, search and rescue; lifeboat and liferaft performance; personal locator beacons

Special Issue Information

Dear Colleagues,

The maritime industry is a highly regulated sector and serves as a cornerstone of global trade and logistics. The safety of ocean-going vessels, whether to fish, carry passengers or cargo, is of paramount importance to both owners and state regulatory actors. However, with the increase in marine traffic flow and advances in artificial intelligence (AI) technology, the field of maritime transportation safety and risk assessment is confronted with both significant opportunities and complex challenges.

The aim of this Special Issue, “Maritime Transportation Safety and Risk Management”, is to provide a common platform for academia and industry to engage in discussions by publishing high-quality original research papers, including, but not limited to, the following topics:

  • Systematic, critical and bibliometric review in maritime safety and risk;
  • Maritime transportation safety incidents, accidents, occurrences trends;
  • Assessing the human error contribution to maritime occurrences using Bayesian networks;
  • Risk-informed decision-making under uncertainty in the maritime domain;
  • Advances in maritime and arctic safety and survival;
  • Integration of AI-based decision support tools for maritime survival and emergency response;
  • Artificial intelligence integration in maritime transportation safety;
  • Maritime transportation accident learning systems: towards an AI-powered safety culture;
  • Arctic maritime risk modelling under climate change scenarios;
  • Cyber risk assessment framework for autonomous and digitized vessels;
  • Quantifying organizational and cultural factors in maritime risk tolerance;
  • Data-driven predictive models for maritime risk assessment.

Dr. Francis Obeng
Dr. Rob Brown
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • maritime safety
  • risk management
  • artificial intelligence (AI)
  • predictive models
  • human error analysis
  • Bayesian networks
  • accident prevention
  • risk-based decision-making
  • emergency response
  • operational safety

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

24 pages, 899 KB  
Article
Development of a Domain-Specific Framework for Analysing Human and Organisational Factors in Tanker Cargo Operations
by Ivan Krivokapić and Nermin Hasanspahić
J. Mar. Sci. Eng. 2026, 14(9), 844; https://doi.org/10.3390/jmse14090844 - 30 Apr 2026
Viewed by 230
Abstract
Tanker cargo operations involve hazardous cargo environments, complex technical systems and stringent operational procedures. These conditions make accident analysis particularly demanding and require analytical approaches that consider the specific operational context of tanker cargo handling. Existing Human Factors Analysis and Classification System (HFACS) [...] Read more.
Tanker cargo operations involve hazardous cargo environments, complex technical systems and stringent operational procedures. These conditions make accident analysis particularly demanding and require analytical approaches that consider the specific operational context of tanker cargo handling. Existing Human Factors Analysis and Classification System (HFACS) adaptations used in maritime safety research provide a useful framework for analysing human and organisational factors, but they do not fully capture the operational characteristics of tanker cargo operations. As a result, some factors specific to tanker cargo handling remain insufficiently represented in existing HFACS-based analyses. Therefore, this study develops and validates a domain-specific HFACS framework for tanker cargo operations (HFACS-TCO) and applies it to the analysis of accident investigation reports. The framework was developed through an iterative process based on accident report analysis, expert evaluation and the development of structured coding guidelines. The reliability of the coding procedure was assessed using Fleiss’s kappa coefficient to evaluate inter-rater agreement. The proposed framework extends existing HFACS adaptations by incorporating cargo operation-specific organisational, operational and environmental factors. A total of 27 accident investigation reports related to tanker cargo operations were analysed. From these reports, 333 causal factors were identified and classified using the HFACS-TCO framework. The results show that tanker cargo accidents rarely arise from a single cause and usually involve multiple interacting organisational, operational and human factors. Most factors were identified at the levels of Preconditions for Unsafe Acts, Organisational Influences and External Factors, indicating that many accident conditions are established before unsafe acts occur at the operational level. The analysis also shows that most accidents involve factors across several HFACS levels, indicating that tanker cargo incidents develop through interactions between different system levels. The proposed HFACS-TCO framework provides a structured, domain-specific approach to analysing tanker cargo accidents and supports a more systematic identification of organisational and human factors in tanker cargo-related operations. Full article
(This article belongs to the Special Issue Maritime Transportation Safety and Risk Management)
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23 pages, 4568 KB  
Article
Risk Assessment of Dynamic Positioning Operations: Modelling the Contribution of Human Factors
by Mykyta Chervinskyi, Francis Obeng, Sidum Adumene and Robert Brown
J. Mar. Sci. Eng. 2026, 14(5), 462; https://doi.org/10.3390/jmse14050462 - 28 Feb 2026
Viewed by 489
Abstract
Dynamic positioning (DP) systems are essential to maritime operations, as they ensure precise station keeping. Yet human error remains a major contributor to DP incidents, often interacting with technical failures and environmental conditions. This study proposes an adaptive probabilistic framework to characterise human-error [...] Read more.
Dynamic positioning (DP) systems are essential to maritime operations, as they ensure precise station keeping. Yet human error remains a major contributor to DP incidents, often interacting with technical failures and environmental conditions. This study proposes an adaptive probabilistic framework to characterise human-error contributions to DP risk and support targeted mitigation. We compare integrated Bayesian network (BN)/fuzzy analytic hierarchy process (AHP) and Bayesian network (BN)/Dempster–Shafer (D-S) theory to model causal relationships, aggregate uncertain expert judgements, and prioritise risk factors. Historical incident narratives, accident reports, and expert elicitation inform the model to analyse failure propagation and quantify factor contributions. In a representative DP case application, insufficient training, operator fatigue, and reduced situational awareness—together with software anomalies and adverse environmental loads—emerge as dominant contributors; BN backward analysis corroborates their diagnostic relevance. The approach yields actionable insights for risk reduction, including tailored training programmes, strengthened safety protocols, and integration of real-time monitoring. It provides an auditable, updateable basis for scenario-based training, software/maintenance assurance, and environment-aware operating envelopes, and is readily extendable as new evidence becomes available. Overall, the framework offers practical value for improving safety, operational continuity, and system resilience in DP operations. Full article
(This article belongs to the Special Issue Maritime Transportation Safety and Risk Management)
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19 pages, 7782 KB  
Article
Numerical Investigation on Safety Assessment of Gas Dispersion from Vent Mast for LNG-Powered Vessels
by Zhaowen Wang, Zhangjian Wang and Gang Chen
J. Mar. Sci. Eng. 2025, 13(10), 1892; https://doi.org/10.3390/jmse13101892 - 2 Oct 2025
Viewed by 1198
Abstract
Conducting a safety simulation assessment of gas release from the vent mast during the design stage holds significant importance for ship design and system operation safety on LNG-powered vessels. Based on a large-scale practical LNG-powered vessel, this paper employs the CFD method to [...] Read more.
Conducting a safety simulation assessment of gas release from the vent mast during the design stage holds significant importance for ship design and system operation safety on LNG-powered vessels. Based on a large-scale practical LNG-powered vessel, this paper employs the CFD method to carry out a safety assessment of the natural gas dispersion, and proposes an optimization design method to address the issue where the vent mast height of large-scale LNG-powered vessels fails to meet specifications. The influencing factors of gas dispersion are discussed. The simulation results indicate that the vent mast height, wind direction, and wind velocity significantly affect the gas dispersion behavior. A lower vent mast height results in a greater risk of flammable gas clouds accumulating on the deck surface. Hazards analysis of the 6 m vent mast condition with windless suggests that a cryogenic explosion hazard zone is formed on the deck centered around the mast position, with the maximum gas concentration reaching 30% and the minimum temperature below −55 °C. The gas cloud spreads along the wind direction, and the extension distance is positively correlated with wind speed. With the increase in wind velocity, the height and volume of flammable gas clouds decrease. When the wind speed is 15 m/s, the volume of the flammable gas cloud is less than half of that at 5 m/s and less than one-tenth of that at 0 m/s. Higher wind velocity can notably promote gas diffusion. Full article
(This article belongs to the Special Issue Maritime Transportation Safety and Risk Management)
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28 pages, 7822 KB  
Article
Intelligent Optimization of Waypoints on the Great Ellipse Routes for Arctic Navigation and Segmental Safety Assessment
by Chenchen Jiao, Zhichen Liu, Jiaxin Hou, Jianan Luo and Xiaoxia Wan
J. Mar. Sci. Eng. 2025, 13(8), 1543; https://doi.org/10.3390/jmse13081543 - 11 Aug 2025
Cited by 1 | Viewed by 1262
Abstract
A great ellipse route (GER), as one of the fundamental routes for ocean voyages, directly influences the actual voyage distance and the complexity of vessel maneuvering through the location and number of its waypoints. Against the backdrop of global warming, the melting of [...] Read more.
A great ellipse route (GER), as one of the fundamental routes for ocean voyages, directly influences the actual voyage distance and the complexity of vessel maneuvering through the location and number of its waypoints. Against the backdrop of global warming, the melting of Arctic sea ice has accelerated the opening of the Arctic shipping route. This paper addresses the issue of how to reasonably segment and adopt rhumb line routes to approximate the GER in the special navigational environment of the Arctic. Using historical routes, recommended routes, and geospatial data that have passed through the Arctic shipping lane as constraints, this paper proposes a waypoint optimization model based on an adaptive hybrid particle swarm optimization-genetic algorithm (AHPSOGA). Additionally, by integrating Arctic remote sensing ice condition data and the Polar Operational Limit Assessment Risk Indexing System (POLARIS), a safety assessment model tailored for this route has been developed, enabling the quantification of sea ice risks and dynamic evaluation of segment safety. Experimental results indicate that the proposed waypoint optimization model reduces the number of waypoints and voyage distance compared to recommended routes and conventional shipping industry methods. Furthermore, the AHPSOGA algorithm achieves a 16.41% and 19.19% improvement in convergence speed compared to traditional GA and PSO algorithms, respectively. In terms of computational efficiency, the average runtime is improved by approximately 12.00% and 14.53%, respectively. The risk levels of each segment of the optimized route are comparable to those of the recommended Northeast Passage route. This study provides an effective theoretical foundation and technical support for intelligent planning and decision-making for Arctic shipping routes. Full article
(This article belongs to the Special Issue Maritime Transportation Safety and Risk Management)
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