Research and Evaluation of Ship Collision Risk

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: closed (31 March 2024) | Viewed by 6782

Special Issue Editors


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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
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E-Mail Website
Guest Editor
Research Group on Safe and Efficient Marine Systems, Marine Technology, Department of Mechanical Engineering, Aalto University, 02150 Espoo, Finland
Interests: safety and systems engineering; risk analysis; maritime safety; winter navigation; autonomous ships
Special Issues, Collections and Topics in MDPI journals
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
Interests: autonomous collision avoidance of ships in order to reduce the incidence of maritime accidents; maritime; geographic information system; shipping; transportation; maritime security; transport management; transportation planning
Special Issues, Collections and Topics in MDPI journals

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

Background:

The research on ship collision risk, its control, and management, have always been the fundamental aspects in the discipline of safety science and maritime transportation. In the meantime, the advancements of the topic also attract industries and create business opportunities to make a contribution to the improvement of the safety of waterborne transportation.

Aim and scope:

The main goal of this Special Issue is to address the development of the research and evaluation of ship collision risk, and its challenges in the process of research and development of ship collision risk on various aspects: new concepts, methodologies, methods, models, and applications, etc.

History:

The research on ship collision risk has always focused on the safety of maritime transport and has been continuously drawing much attention from both academia and industry to develop new methods, models, tools, and systems to improve the accuracy, effectiveness, and reliability of the risk analysis results and facilitate the management of maritime safety.

Cutting-edge research:

Data-drive ship collision risk modeling, knowledge-based decision making, autonomous ship collision avoidance, risk-based design of autonomous ships, etc.

What kind of paper we are looking for:

Literature review, bibliometric analysis, research papers, etc. that focus on new insights, methodologies, methods, models and applications of research and evaluation of ship collision risks.

Dear Colleagues,

Waterborne transportation has been playing a significant role in the development of the global economy. A series of new technologies, new concepts, and a continuously growing number of ships brought new realities to the development of the shipping industry and waterborne transportation. However, maritime accident, especially ship collision accident has been posing a threat to individual, societies, and industries from various aspects due to their dire consequences in terms of loss of life, economic loss, environmental pollution, and negative impact on the logistic network and social awareness, etc.

To address the new advancements in the field of research and evaluation of ship collision risk, the focus of this Special Issue will be devoted to the new insights and experiences, that are promising to share among the researchers and developers in this field. The topics of interest for this Special Issue include, but are not limited to, the following aspects:

  • Literature review, bibliometric analysis, etc. on the research and evaluation of ship collision risk.
  • New methods for ship collision risk modeling for the individual ship.
  • Risk-based decision-making, path planning, and control for the individual ship.
  • Risk modeling and collision avoidance in complicated scenarios, e.g., inland navigation, multi-ship encounters, etc.
  • Data-driven methods or applications of artificial intelligence on ship collision risk analysis.
  • New methods and insights on regional ship collision risk analysis, modeling, and management.
  • Risk-based ship behavior analysis.
  • The new method, insights, and models on causation analysis of ship collision accidents.
  • Methods, models, and applications of accident-based research and evaluation of ship collision risk.
  • Safety management of ship collision risk.
  • Ship collision risk research related to autonomous ships, e.g., risk analysis of the design of the autonomous ship, and risk-based decision-making system for the autonomous ship.
  • Ship collision risk in extreme environments (e.g, ship navigation in ice and polar areas)

Dr. Pengfei Chen
Dr. Osiris Valdez Banda
Dr. Mengxia Li
Dr. Lei Du
Guest Editors

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Keywords

  • ship collision risk
  • risk analysis
  • risk evaluation
  • collision avoidance
  • decision making
  • AIS
  • artificial intelligence
  • big data
  • autonomous ship
  • extreme environment
  • maritime safety

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Published Papers (4 papers)

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Research

26 pages, 4960 KiB  
Article
An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller
by Xin Shi, Pengfei Chen and Linying Chen
J. Mar. Sci. Eng. 2023, 11(12), 2294; https://doi.org/10.3390/jmse11122294 - 3 Dec 2023
Cited by 2 | Viewed by 1354
Abstract
Due to the influence of the natural environment, it is very challenging to control the movement of ships to navigate safely and avoid potential risks induced by external environmental factors, especially for the development of autonomous ships in inland or restricted waterways. In [...] Read more.
Due to the influence of the natural environment, it is very challenging to control the movement of ships to navigate safely and avoid potential risks induced by external environmental factors, especially for the development of autonomous ships in inland or restricted waterways. In this research, we propose an integrated approach for ship heading control that improves the timeliness and robustness of navigation. Recursive least squares and backward propagation neural networks are utilized to identify the ship motion model parameters under the influence of external factors and predict their development in real time. A particle swarm optimization-integrated Fractional Order Proportion Integration Differentiation (FOPID) controller is then designed based on the dynamically identified motion model to achieve accurate heading control for ships navigating in restricted waterways. A case study was conducted based on the Korea Venture Large Crude Carrier 2 (KVLCC2) model to verify the effectiveness, and a comparison between the conventional FOPID controller and the improved FOPID controller was also conducted. The results indicate that the proposed identification–prediction–optimization FOPID controller has faster speed on stabilization and has higher robustness against external influences, which could provide added value for the development of a motion controller for the autonomous ship for inland and restricted waterway navigation. Full article
(This article belongs to the Special Issue Research and Evaluation of Ship Collision Risk)
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21 pages, 8583 KiB  
Article
A Domain-Based Model for Identifying Regional Collision Risk and Depicting Its Geographical Distribution
by Zihao Liu, Zhaolin Wu, Zhongyi Zheng, Xianda Yu, Xiaoxuan Bu and Wenjun Zhang
J. Mar. Sci. Eng. 2023, 11(11), 2092; https://doi.org/10.3390/jmse11112092 - 31 Oct 2023
Cited by 1 | Viewed by 1083
Abstract
In recent years, the increasing volume and complexity of ship traffic has raised the probability of collision accidents in ports, waterways, and coastal waters. Due to the relative rarity of collision accidents, near misses have been used in the research to study the [...] Read more.
In recent years, the increasing volume and complexity of ship traffic has raised the probability of collision accidents in ports, waterways, and coastal waters. Due to the relative rarity of collision accidents, near misses have been used in the research to study the collision risk in the relevant water areas. However, the factor of near miss identification is usually limited to the relative distance between ships, and the instantaneous quantification and geographical distribution of collision risk is not paid enough attention. Therefore, this article proposed a domain-based regional collision risk model that can quantify the collision risk by detecting near miss scenarios. The proposed model is capable of quantifying the collision risk in the water area instantaneously and periodically and can be used to depict the geographical distribution of collision risks in combination with a grid method and the spatial interpolation technique. To validate the proposed model, some experimental case studies were carried out using automatic identification system (AIS) data from the Bohai Strait. The results show the capability and advantage of the proposed model in regional collision risk identification and visualization, which is helpful for maritime surveillance when monitoring and organizing ship traffic and may therefore contribute to the improvement of maritime safety. Full article
(This article belongs to the Special Issue Research and Evaluation of Ship Collision Risk)
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21 pages, 4057 KiB  
Article
A Hybrid Probabilistic Risk Analytical Approach to Ship Pilotage Risk Resonance with FRAM
by Yunlong Guo, Shenping Hu, Yongxing Jin, Yongtao Xi and Wei Li
J. Mar. Sci. Eng. 2023, 11(9), 1705; https://doi.org/10.3390/jmse11091705 - 29 Aug 2023
Cited by 3 | Viewed by 1417
Abstract
Collision risk in ship pilotage process has complex characteristics that are dynamic, uncertain, and emergent. To reveal collision risk resonance during ship pilotage process, a hybrid probabilistic risk analysis approach is proposed, which integrates the Functional Resonance Analysis Method (FRAM), Dempster–Shafer (D–S) evidence [...] Read more.
Collision risk in ship pilotage process has complex characteristics that are dynamic, uncertain, and emergent. To reveal collision risk resonance during ship pilotage process, a hybrid probabilistic risk analysis approach is proposed, which integrates the Functional Resonance Analysis Method (FRAM), Dempster–Shafer (D–S) evidence theory, and Monte Carlo (MC) simulation. First, FRAM is used to qualitatively describe the coupling relationship and operation mechanism among the functions of the pilotage operation system. Then, the D–S evidence theory is used to determine the probability distribution of the function output in the specified pilotage scenario after quantitatively expressing the function variability, coupling effect, and the influence of operation conditions through rating scales. Finally, MC simulation is used to calculate the aggregated coupling variability between functions, and the critical couplings and risk resonance paths under different scenarios are identified by setting the threshold and confidence level. The results show that ship collision risk transmission is caused by function resonance in the pilotage system, and the function resonance paths vary with pilotage scenarios. The critical coupling ‘F2-F7(I)’ emerges as a consistent factor in both scenarios, emphasizing the significance of maintaining a proper lookout. The hybrid probabilistic risk analytical approach to ship pilotage risk resonance with FRAM can be a useful method for analysing the causative mechanism of ship operational risk. Full article
(This article belongs to the Special Issue Research and Evaluation of Ship Collision Risk)
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21 pages, 3894 KiB  
Article
Determining the Proper Times and Sufficient Actions for the Collision Avoidance of Navigator-Centered Ships in the Open Sea Using Artificial Neural Networks
by Jong-Kwan Kim and Deuk-Jin Park
J. Mar. Sci. Eng. 2023, 11(7), 1384; https://doi.org/10.3390/jmse11071384 - 7 Jul 2023
Cited by 7 | Viewed by 2025
Abstract
Ship collisions are a major maritime accident; various systems have been proposed to prevent them. Through investigating and analyzing the causes of maritime accidents, it has been established that ship collisions can either caused by delaying actions or not taking the sufficient actions [...] Read more.
Ship collisions are a major maritime accident; various systems have been proposed to prevent them. Through investigating and analyzing the causes of maritime accidents, it has been established that ship collisions can either caused by delaying actions or not taking the sufficient actions to avoid them. Recognizing the limitations in providing quantitative numerical values for avoiding ship collisions, this study aimed to use Bayesian regularized artificial neural networks (BRANNs) to suggest the proper time and sufficient actions required for ship collision avoidance consistent with the Convention on the International Regulations for Preventing Collisions at Sea. We prepared the data by calculating the proper times and sufficient actions based on precedent research and used them to train, validate, and assess the BRANNs. Subsequently, an artificial neural network controller was designed and proposed. The data of the proposed neural network controller were verified via simulation, validating the controller. This study is limited in cases such as overtaking a ship in front. However, it is expected that this controller can be improved by establishing the criteria for an appropriate overtaking distance after further examining the closest point of approach (CPA) and time to the CPA (TCPA) for overtaking a ship in front and using the method presented herein. Full article
(This article belongs to the Special Issue Research and Evaluation of Ship Collision Risk)
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