Automatic Identification System-Based Approach for Assessing the Near-Miss Collision Risk Dynamics of Ships in Ports

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

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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.

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A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification

Polish Maritime Research ◽

10.2478/pomr-2018-0017 ◽

2018 ◽

Vol 25 (s1) ◽

pp. 14-21 ◽

Cited By ~ 5

Author(s):

Rafał Szłapczyński ◽

Tacjana Niksa-Rynkiewicz

Keyword(s):

Risk Measures ◽

Risk Measure ◽

Near Miss ◽

Fuzzy Classification ◽

Automatic Identification ◽

Identification System ◽

Collision Risk ◽

Neuro Fuzzy ◽

Fuzzy Network

Abstract Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near-miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS.

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Simulation-Based Estimation of Collision Risk During Ship Maneuvering in Two-Lane Canal Using Mathematical Maneuvering Group Model and Automatic Identification System Data

Volume 4A: Structures, Safety and Reliability ◽

10.1115/omae2014-23768 ◽

2014 ◽

Cited By ~ 1

Author(s):

I. Putu Sindhu Asmara ◽

Eiichi Kobayashi ◽

Ketut Buda Artana ◽

Agoes A. Masroeri ◽

Nobukazu Wakabayashi

Keyword(s):

Research Area ◽

Automatic Identification ◽

Identification System ◽

Group Model ◽

Collision Risk ◽

Ship Maneuvering ◽

Automatic Identification System ◽

Mmg Model ◽

Simulation Based ◽

The Impact

This paper proposes a simulation-based method to estimate collision risk for a ship operating in a two-lane canal. According to rule 9 of the Colreg-72 navigation rules, in a narrow canal, a vessel shall keep as near to the wall that lies on its starboard side. However, a busy harbor entered through a narrow canal still presents impact hazards. Certain conditions in a two-lane canal, such as a head-on situation in the straight part of the canal during an overtaking maneuver and large curvature of a turning maneuver in the bend part of the canal, could lead to accidents. In the first condition, the ship alters its own course to the port side to overtake another ship in the same lane but the course altered is too large and hits the wall of the canal. In the second condition, the target ship may take an excessively large turn on the bend part of the canal, causing collision with the ship on the opposite lane. Collision risk is represented as the risk of damage to the ship structure and includes the probability of impact accident and severity of structural damage. Predictions of collision probabilities in a two-lane canal have been developed based on a simulation of ship maneuvering using a mathematical maneuvering group (MMG) model and automatic identification system (AIS) data. First, the propeller revolution and rudder angle of the subject ship are simulated to determine safe trajectories in both parts of the canal. Second, impact accidents are simulated for both conditions. The ship’s speed, and current and wind velocity are randomly simulated based on the distribution of the AIS and environment data for the research area. The structural consequences of the impact accident are measured as collision energy losses, based on the external dynamics of ship collision. The research area of the two-lane canal is located at the Madura Strait between the Java and Madura islands in East Java of Indonesia, as shown by the red line in Figure 1. A project for developing a new port and dredging a new two-lane canal to facilitate an increase in the number of ship calls is currently underway in the research area. Figure 1 shows the ships’ trajectories plotted using the AIS data as on January 1, 2011. The trajectories are mostly seen to be coming out of the canal, confirming that it is shallow and needs to be dredged.

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