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Recent Advances in Maritime Safety and Ship Collision Avoidance
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Recent Advances in Maritime Safety and Ship Collision Avoidance

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 September 2025 | Viewed by 1499

Special Issue Editors

Prof. Dr. Pengfei Chen

E-Mail Website
Guest Editor
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: maritime safety; ship collision risk; risk analysis; autonomous ship; AIS; big data
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junmin Mou

E-Mail Website
Guest Editor
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
Interests: ocean engineering; AIS
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Du

E-Mail Website
Guest Editor
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: maritime traffic risk; evaluation of risk and safety of maritime transportation with respect to manned and unmanned vessels
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Safety is a continuous focus for the maritime transportation industry. With the recent development and recovery of the global economy, maritime transportation has been playing an increasingly crucial role in the global logistical network. In the meantime, maintaining the safety of maritime transportation and reducing the potential for accidents, are concerns that have seen increased attention from both industry and academia, especially with the occurrence of autonomous shipping and artificial intelligence.

The main goal of this Special Issue is to address the state-of-the-art development of the research on maritime safety and ship collision avoidance on various aspects: new concepts, methodologies, methods, models, and applications, etc. The topic of interest for this Special Issue includes, but is not limited to, the following aspects:

  • Literature review, bibliometric analysis, etc. on the research of maritime safety and ship collision avoidance.
  • New methods for ship collision risk modeling for the individual ship.
  • Risk-based decision-making, path planning, and collision avoidance for the individual ship.
  • Risk modeling and collision avoidance in complicated scenarios, e.g., inland navigation, multi-ship encounters, etc.
  • New methods and insights on regional ship collision risk analysis, modeling, and management.
  • The new method, insights, and models on causation analysis of ship collision accidents.
  • Ship collision avoidance research related to autonomous ships, e.g., autonomous collision avoidance, cooperative collision avoidance decision-making and control, etc.

Prof. Dr. Pengfei Chen
Prof. Dr. Junmin Mou
Dr. Lei Du
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 analysis
  • collision avoidance
  • decision making
  • AIS
  • artificial intelligence
  • autonomous ship

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

Published Papers (3 papers)

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Research

23 pages, 2337 KiB  
Article
Analyzing the Causation of Collision Accidents Between Merchant and Fishing Vessels in China’s Coastal Waters by Integrating Association Rules and Complex Networks
by Qiaoling Du, Xiaoxue Ma, Ruiwen Zhang and Weiliang Qiao
J. Mar. Sci. Eng. 2025, 13(6), 1086; https://doi.org/10.3390/jmse13061086 - 29 May 2025
Viewed by 172
Abstract
The frequent occurrence of collision accidents between merchant and fishing vessels in China’s offshore waters not only threatens human lives and property, but also disrupts shipping and fishing activities and may cause marine environmental pollution. To effectively reduce such accidents and increase maritime [...] Read more.
The frequent occurrence of collision accidents between merchant and fishing vessels in China’s offshore waters not only threatens human lives and property, but also disrupts shipping and fishing activities and may cause marine environmental pollution. To effectively reduce such accidents and increase maritime safety in Chinese coastal waters, this study integrates association rules with complex networks to develop a directed weighted network of causal factors. Grounded theory and the Human Factors Analysis and Classification System (HFACS) are applied to identify and categorize causal factors from 152 collision accident investigation reports. Potential causal relationships are mined using the association rule, which is then applied to construct the causal network. Finally, the topological characteristics of the network are analyzed. The results reveal that serious negligence in lookout, failure to assess collision risks properly, and failure to adopt a safe speed significantly impact collision accidents. These findings highlight the necessity of implementing targeted preventive measures to address critical factors. This study provides valuable insights for maritime stakeholders to develop effective strategies. Full article
(This article belongs to the Special Issue Recent Advances in Maritime Safety and Ship Collision Avoidance)
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19 pages, 3095 KiB  
Article
An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
by Kun Yang, Jinglong Lin, Jianjun Ding, Bing Zheng and Li Qin
J. Mar. Sci. Eng. 2025, 13(6), 1049; https://doi.org/10.3390/jmse13061049 - 26 May 2025
Viewed by 328
Abstract
Fishing vessels are essential for the activities of catching, moving, and storing fish. However, fishing vessel accidents claim thousands of deaths every year. This study presents a novel integrated safety monitoring and early warning system designed for fishing vessels, offering significant advancements in [...] Read more.
Fishing vessels are essential for the activities of catching, moving, and storing fish. However, fishing vessel accidents claim thousands of deaths every year. This study presents a novel integrated safety monitoring and early warning system designed for fishing vessels, offering significant advancements in maritime safety through real-time alerts based on vessel attitude motion and environmental conditions. The innovation of the system lies in its dual-subsystem architecture: a sensing terminal equipped with a nine-axis sensor, temperature and humidity sensors, a GPS module, and a surveillance camera collects critical data, while a decision support subsystem processes this information via a fuzzy logic-based algorithm to generate a “danger score”. This score quantifies the vessel’s safety status, enabling the system to trigger alerts through SMS and web notifications when predefined thresholds are exceeded. Field trials in the Zhoushan Sea area confirmed the system’s effectiveness in accurately predicting safety hazards and providing timely alerts. The results highlight its potential to enhance operational safety and contribute to the digitization of fisheries management by offering reliable real-time data on vessel conditions. The system’s modular and cost-efficient design ensures it is scalable and adaptable for widespread use across the fishing industry. Our study addresses the limitations of existing technologies by providing a balanced solution that combines comprehensive sensing capabilities with real-time responsiveness and cost-effectiveness, offering a practical and innovative approach to improve fishing vessel safety. Full article
(This article belongs to the Special Issue Recent Advances in Maritime Safety and Ship Collision Avoidance)
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55 pages, 18379 KiB  
Article
Maritime Risk Assessment: A Cutting-Edge Hybrid Model Integrating Automated Machine Learning and Deep Learning with Hydrodynamic and Monte Carlo Simulations
by Egemen Ander Balas and Can Elmar Balas
J. Mar. Sci. Eng. 2025, 13(5), 939; https://doi.org/10.3390/jmse13050939 - 11 May 2025
Viewed by 499
Abstract
In this study, a Hybrid Maritime Risk Assessment Model (HMRA) integrating automated machine learning (AML) and deep learning (DL) with hydrodynamic and Monte Carlo simulations (MCS) was developed to assess maritime accident probabilities and risks. The machine learning models of Light Gradient Boosting [...] Read more.
In this study, a Hybrid Maritime Risk Assessment Model (HMRA) integrating automated machine learning (AML) and deep learning (DL) with hydrodynamic and Monte Carlo simulations (MCS) was developed to assess maritime accident probabilities and risks. The machine learning models of Light Gradient Boosting (LightGBM), XGBoost, Random Forest, and Multilayer Perceptron (MLP) were employed. Cross-validation of model architectures, calibrated baseline configurations, and hyperparameter optimization enabled predictive precision, producing generalizability. This hybrid model establishes a robust maritime accident probability prediction framework through a multi-stage methodology that ensembles learning architecture. The model was applied to İzmit Bay (in Türkiye), a highly jammed maritime area with dense traffic patterns, providing a complete methodology to evaluate and rank risk factors. This research improves maritime safety studies by developing an integrated, simulation-based decision-making model that supports risk assessment actions for policymakers and stakeholders in marine spatial planning (MSP). The potential spill of 20 barrels (bbl) from an accident between two tankers was simulated using the developed model, which interconnects HYDROTAM-3D and the MCS. The average accident probability in İzmit Bay was estimated to be 5.5 × 10−4 in the AML based MCS, with a probability range between 2.15 × 10−4 and 7.93 × 10−4. The order of the predictions’ magnitude was consistent with the Undersecretariat of the Maritime Affairs Search and Rescue Department accident data for İzmit Bay. The spill reaches the narrow strait of the inner basin in the first six hours. This study determines areas within the bay at high risk of accidents and advocates for establishing emergency response centers in these critical areas. Full article
(This article belongs to the Special Issue Recent Advances in Maritime Safety and Ship Collision Avoidance)
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