Potential risk ship domain as a danger criterion for real-time ship collision risk evaluation

2019 ◽  
Vol 194 ◽  
pp. 106610 ◽  
Author(s):  
Namkyun Im ◽  
Tu Nam Luong
Keyword(s):  
Real Time ◽  
Potential Risk ◽  
Risk Evaluation ◽  
Collision Risk ◽  
Ship Collision

scholarly journals Multi-Ship Collision Avoidance Decision-Making Based on Collision Risk Index

2020 ◽  
Vol 8 (9) ◽  
pp. 640
Author(s):  
Yingjun Hu ◽  
Anmin Zhang ◽  
Wuliu Tian ◽  
Jinfen Zhang ◽  
Zebei Hou
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Risk Analysis ◽  
Real Time ◽  
Collision Avoidance ◽  
Evaluation Method ◽  
Risk Index ◽  
Collision Risk ◽  
Ship Collision ◽  
Risk Thresholds

Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

scholarly journals A Velocity Obstacle-Based Real-Time Regional Ship Collision Risk Analysis Method

2021 ◽  
Vol 9 (4) ◽  
pp. 428
Author(s):  
Pengfei Chen ◽  
Mengxia Li ◽  
Junmin Mou
Keyword(s):  
Risk Analysis ◽  
Real Time ◽  
Human Life ◽  
Identification System ◽  
Analysis Method ◽  
Collision Risk ◽  
Time Data ◽  
Maritime Safety ◽  
Ship Collision ◽  
Velocity Obstacle

Maritime accidents such as ship collisions pose continuous risks to individuals and society with due to their severe consequences on human life, economic and environmental losses, etc. Supervising the maritime traffic in the different regions and maintaining its safety level is an essential task for stakeholders such as maritime safety administrations. In this research, a new ship collision risk analysis method is developed with the utilisation of AIS (Automatic Identification System) data. A velocity obstacle-based risk measurement is applied to measure the risk of collision between multiple ships from the velocity perspective, based on which, the collision risk and the complexity of the encounter situation are obtained at the same time. Secondly, a density-based clustering technique is introduced to identify the hotspots of ship traffic in the region as an indicator for maritime safety operators. A case study using historical AIS data was implemented to verify the effectiveness of the proposed approach in a manner that simulates the real-time data scenario. Furthermore, a comparison between existing risk analysis method is conducted to validate the proposed method.

scholarly journals A Survey on Ship Collision Risk Evaluation

2014 ◽  
Vol 26 (6) ◽  
pp. 475-486 ◽  
Author(s):  
Qingyang Xu ◽  
Ning Wang
Keyword(s):  
Collision Avoidance ◽  
Visual Information ◽  
Risk Evaluation ◽  
Numerical Models ◽  
Electrical Equipment ◽  
Maritime Transportation ◽  
Collision Risk ◽  
Human Errors ◽  
Decision Supporting ◽  
Ship Collision

Recently, ship collision avoidance has become essential due to the emergence of special vessels like chemical tankers and VLCCs (very large crude carriers), etc. The information needed for safe navigation is obtained by combining electrical equipment with real-time visual information. However, misjudgements and human errors are the major cause of ship collisions according to research data. The decision support system of Collision avoidance is an advantageous facility to make up for this. Collision risk evaluation is one of the most important problems in collision avoidance decision supporting system. A review is presented of different approaches to evaluate the collision risk in maritime transportation. In such a context, the basic concepts and definitions of collision risk and their evaluation are described. The review focuses on three categories of numerical models of collision risk calculation: methods based on traffic flow theory, ship domain and methods based on dCPA and tCPA.

scholarly journals An Estimation of Ship Collision Risk Based on Relevance Vector Machine

2021 ◽  
Vol 9 (5) ◽  
pp. 538
Author(s):  
Jinwan Park ◽  
Jung-Sik Jeong
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Risk Estimation ◽  
Good Method ◽  
Relevance Vector Machine ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Collision Risk ◽  
Estimation Model ◽  
Ship Collision

According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

scholarly journals Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Lei Du ◽  
Osiris A. Valdez Banda ◽  
Floris Goerlandt ◽  
Pentti Kujala ◽  
Weibin Zhang
Keyword(s):  
Baltic Sea ◽  
Perceived Risk ◽  
Maritime Transportation ◽  
Near Miss ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Ship Collision ◽  
The Impact ◽  
Ship Behavior

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

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