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: 5 December 2025 | Viewed by 432

Special Issue Editors


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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
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

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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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Published Papers (1 paper)

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Research

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
Viewed by 303
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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