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Advancements in Maritime Safety and Risk Assessment
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Advancements in Maritime Safety and Risk Assessment

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 1289

Special Issue Editors

Dr. Xinjian Wang

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian 116026, China
Interests: ship navigation and maritime technology; marine traffic safety and environment
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dracos Vassalos

E-Mail Website
Guest Editor
Department of Naval Architecture, Ocean & Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
Interests: maritime safety and risk; ship dynamics and stability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global maritime transportation system serves as a crucial pillar of international trade. With the rapid increase in marine traffic flow and advancements in artificial intelligence (AI) technology, the field of maritime safety and risk management is encountering both significant opportunities and complex challenges. Notably, the widespread adoption of digital technologies, automated systems, and big data analytics has transformed the maritime sector, introducing novel research directions and practical demands. In this context, this Special Issue seeks to showcase cutting-edge theoretical research and practical applications in maritime safety and risk assessment, offering scientific insights to support the efficient, safe, and sustainable development of the shipping industry.

This Special Issue invites high-quality original research papers, including, but not limited to, the following topics:

  • Modeling and prediction of marine traffic flow;
  • Maritime cybersecurity and detection of anomalous behaviors;
  • Identification of unusual ship activities and risk warning systems;
  • Analysis of shipping big data and intelligent decision-making support;
  • Artificial intelligence-based optimization for ship navigation safety;
  • Innovative applications of digital technologies in maritime safety;
  • Integration of intelligent and automation technologies in shipping system;
  • Sustainable maritime transportation and environmental impact assessments;
  • Damage stability analyses using model experiments and numerical simulations

This Special Issue aims to provide a communication platform for academia and industry, fostering multidisciplinary integration and technological innovation in maritime safety and risk assessment. It seeks to support the shipping industry in achieving efficient and resilient development within an increasingly complex environment.

Dr. Xinjian Wang
Prof. Dr. Dracos Vassalos
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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 assessment
  • traffic flow prediction
  • cybersecurity
  • intelligent decision support
  • big data analysis
  • sustainable maritime transport
  • digital technologies
  • model experiments
  • numerical simulations

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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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (3 papers)

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Research

31 pages, 2914 KiB  
Article
Ship-To-Ship Liquefied Natural Gas Bunkering Risk Assessment by Integrating Fuzzy Failure Mode and Effect Analysis and the Technique for Order Preference by Similarity to an Ideal Solution
by Wei Feng, Zichun Wang, Xirui Dai, Shengli Dong, Weiliang Qiao and Xiaoxue Ma
J. Mar. Sci. Eng. 2025, 13(4), 710; https://doi.org/10.3390/jmse13040710 - 2 Apr 2025
Viewed by 32
Abstract
An increasing demand can be observed in ship-to-ship (STS) liquefied natural gas (LNG) bunkering operations, and the failures involved may lead to considerable casualties or environmental damage. For this purpose, a comprehensive methodology is proposed in this study to identify and assess these [...] Read more.
An increasing demand can be observed in ship-to-ship (STS) liquefied natural gas (LNG) bunkering operations, and the failures involved may lead to considerable casualties or environmental damage. For this purpose, a comprehensive methodology is proposed in this study to identify and assess these failure modes. In detail, the STS LNG bunkering process is first decomposed to develop a hierarchical structure according to systems-theoretic process analysis (STPA), the results of which serve to identify potential failure modes and their causes. Then, all the failure modes are evaluated by experts in terms of occurrence, severity, and detectability to develop a fuzzy confidential matrix, which is then transferred as an explicit confidential matrix to be weighted and normalized. Finally, the risk levels of these failure modes are analyzed by relative closeness obtained from the technique for order preference by similarity to an ideal solution (TOPSIS). This study determines nine failure modes, all of which are ranked in terms of risk level. “High pressure in vapor return line”, and “High flow rate and leakage of LNG” are determined as the top two failure modes, with risk closeness values of 0.5791 and 0.5728, respectively. “Power failure for emergency valves” is ranked as the last one, with the risk closeness value being 0.5444. Finally, suggestions are proposed according to bunkering operation guidelines to prevent or control these failure modes. Full article
(This article belongs to the Special Issue Advancements in Maritime Safety and Risk Assessment)
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21 pages, 5478 KiB  
Article
Research on Coupling Mechanisms of Risk Factors for Collision Accidents Between Merchant Ships and Fishing Vessels Based on the N-K Model
by Chuanming Dong, Xitong Guo and Yongjun Gong
J. Mar. Sci. Eng. 2025, 13(3), 466; https://doi.org/10.3390/jmse13030466 - 27 Feb 2025
Viewed by 297
Abstract
Preventing collision accidents between merchant ships and fishing vessels has long been a significant challenge for maritime safety in coastal waters. To quantitatively analyze the relationship between the risk factors contributing to these collisions, identify the key factors leading to such accidents, and [...] Read more.
Preventing collision accidents between merchant ships and fishing vessels has long been a significant challenge for maritime safety in coastal waters. To quantitatively analyze the relationship between the risk factors contributing to these collisions, identify the key factors leading to such accidents, and develop effective prevention strategies, the N-K model was employed to examine the risk coupling mechanisms involved. The model was based on an analysis of 132 collision incidents between merchant ships and fishing vessels in China’s coastal waters from 2013 to 2023. The characteristics of these collision accidents were investigated, and the risk factors were categorized into four distinct types: human, management, environmental, and ship factors. The coupling of collision risk factors between merchant ships and fishing vessels was mainly considered from the perspective of the overall system, and the N-K model was used to calculate the probability and risk values associated with the coupling of these four risk factors. Modeling results indicated that the coupling value of four factors was 0.1083, which was 1.5 times greater than the maximum coupling value of three factors and 2.1 times greater than the maximum coupling value of two factors. The risk of collision accidents between merchant ships and fishing vessels increases gradually with an increase in the risk coupling factors. Among the four categories of factors, the risk coupling between the ship factors and environmental factors is associated with a relatively large probability of accidents. Appropriate countermeasures were proposed to implement effective preventive measures at the source of collision accidents. Full article
(This article belongs to the Special Issue Advancements in Maritime Safety and Risk Assessment)
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25 pages, 14201 KiB  
Article
A Dynamic Trajectory Temporal Density Model for Analyzing Maritime Traffic Patterns
by Dapeng Jiang, Guoyou Shi, Lin Ma, Weifeng Li, Xinjian Wang and Guibing Zhu
J. Mar. Sci. Eng. 2025, 13(2), 381; https://doi.org/10.3390/jmse13020381 - 19 Feb 2025
Viewed by 298
Abstract
This study investigates the spatiotemporal density aggregation and pattern distribution of vessel traffic amidst bustling maritime logistics scenarios. Firstly, a relatively new spatiotemporal segmentation and reconstruction method is proposed for ship AIS trajectories to address trajectory disruptions caused by berthing, anchorage, and other [...] Read more.
This study investigates the spatiotemporal density aggregation and pattern distribution of vessel traffic amidst bustling maritime logistics scenarios. Firstly, a relatively new spatiotemporal segmentation and reconstruction method is proposed for ship AIS trajectories to address trajectory disruptions caused by berthing, anchorage, and other factors. Subsequently, a trajectory filtering algorithm utilizing time window panning is introduced to mitigate position jumps and deviation errors in trajectory points, ensuring that the dynamic trajectory adheres to the spatiotemporal correlations of ship motion. Secondly, to establish a geographical spatial mapping of dynamic trajectories, spatial gridding is applied to maritime traffic areas. By associating the geographical space of traffic activities with the temporal attributes of dynamic trajectories, a dynamic trajectory temporal density model is constructed. Finally, a case study is conducted to evaluate the effectiveness and applicability of the proposed method in identifying spatiotemporal patterns of maritime traffic and spatiotemporal density aggregation states. The results show that the proposed method can identify dynamic trajectory traffic patterns after the application of compression algorithms, providing a novel approach to studying the spatiotemporal aggregation of maritime traffic in the era of big data. Full article
(This article belongs to the Special Issue Advancements in Maritime Safety and Risk Assessment)
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