Ship Collision Accidents and Simulation of Small Ship Collision to Fender System of Bridges

Navigational safety is an important issue in maritime transportation. The most frequent type of maritime accident in the port and coastal waters is the ship collision. Although some ship collision models have been developed in the past, few have taken account of wind and sea current effects. However, wind and sea current are critical factors in ship maneuvering. Therefore, based on the previous collision model without wind and sea current effects, this study further develops a ship collision model with wind and sea current effects. Finally, a comparison of the results for the proposed collision model in this study and the ship maneuvering simulator is shown to illustrate the effectiveness of the proposed mathematical model in this paper, followed by the conclusions and suggestions given to navigators, port managers, and governmental maritime departments to improve navigational safety in port and coastal waters.

Download Full-text

Quantitative Ship Collision Frequency Estimation Models: A Review

Journal of Marine Science and Engineering ◽

10.3390/jmse9050533 ◽

2021 ◽

Vol 9 (5) ◽

pp. 533

Author(s):

Mirko Čorić ◽

Sadko Mandžuka ◽

Anita Gudelj ◽

Zvonimir Lušić

Keyword(s):

Risk Assessment ◽

Collision Frequency ◽

Frequency Estimation ◽

Cost Effective ◽

Future Improvement ◽

Ship Collision ◽

Estimation Models

Ship collisions are one of the most common types of maritime accidents. Assessing the frequency and probability of ship collisions is of great importance as it provides a cost-effective and practical way to mitigate risk. In this paper, we present a review of quantitative ship collision frequency estimation models for waterway risk assessment, accompanied by a classification of the models and a description of their main modelling characteristics. Models addressing the macroscopic perspective in the estimation of ship collision frequency on waterways are reviewed in this paper with a total of 29 models. We extend the existing classification methodology and group the collected models accordingly. Special attention is given to the criteria used to detect potential ship collision candidates, as well as to causation probability and the correlation of models with real ship collision statistics. Limitations of the existing models and future improvement possibilities are discussed. The paper can be used as a guide to understanding current achievements in this field.

Download Full-text

A Spatiotemporal Statistical Method of Ship Domain in the Inland Waters Driven by Trajectory Data

Journal of Marine Science and Engineering ◽

10.3390/jmse9040410 ◽

2021 ◽

Vol 9 (4) ◽

pp. 410

Author(s):

Fan Zhang ◽

Xin Peng ◽

Liang Huang ◽

Man Zhu ◽

Yuanqiao Wen ◽

...

Keyword(s):

Statistical Method ◽

Yangtze River ◽

Domain Size ◽

Inland Waters ◽

The Yangtze River ◽

Trajectory Data ◽

Different Types ◽

Dry Seasons ◽

Ship Collision ◽

Shape And Size

In this study, a method for dynamically establishing ship domain in inland waters is proposed to help make decisions about ship collision avoidance. The surrounding waters of the target ship are divided to grids and then calculating the grid densities of ships in each moment to determine the shape and size of ship domain of different types of ships. At last, based on the spatiotemporal statistical method, the characteristics of ship domains of different types of ship in different navigational environments were analyzed. The proposed method is applied to establish ship domains of different types of ship in Wuhan section of the Yangtze River in January, February, July, and August in 2014. The results show that the size of ship domain increases as the ship size increases in each month. The domain size is significantly influenced by the water level, and the ship domain size in dry seasons is larger than in the wet seasons of inland waters.

Download Full-text

An Estimation of Ship Collision Risk Based on Relevance Vector Machine

Journal of Marine Science and Engineering ◽

10.3390/jmse9050538 ◽

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.

Download Full-text

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

Journal of Marine Science and Engineering ◽

10.3390/jmse9020180 ◽

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.

Download Full-text