scholarly journals Ship classification based on random forest using static information from AIS data

2021 ◽  
Vol 2113 (1) ◽  
pp. 012072
Author(s):  
Yitao Wang ◽  
Lei Yang ◽  
Xin Song ◽  
Xuan Li
Keyword(s):  
Random Forest ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Transportation ◽  
Automatic Identification System ◽  
Related Data ◽  
Static Information ◽  
Ship Classification ◽  
Type Information

Abstract With the wide use of automatic identification system (AIS), a large amount of ship-related data has been provided for marine transportation analysis. Generally, AIS reports the type information of ships, but there are still many ships with type unknown in AIS data. It is necessary to develop algorithms which can identify ship type from AIS data. In this paper, we employ random forest to classify ships according to the static information from AIS messages. Moreover, the importance of static features is discussed, which explains the reason why some classes of ships are misclassified. The method of this paper is proved to be effective in ship classification using static information.

Automatic Identification System Receiver for Small Fishing Vessels

2020 ◽  
Author(s):  
Febus Reidj G. Cruz ◽  
Jeremiah A. Ordiales ◽  
Malvin Angelo C. Reyes ◽  
Pinky T. Salvanera
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Fishing Vessels

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

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.

Comprehensive Analysis of Automatic Identification System (AIS) Data in Regard to Vessel Movement Prediction

2014 ◽  
Vol 67 (5) ◽  
pp. 791-809 ◽  
Author(s):  
Philipp Last ◽  
Christian Bahlke ◽  
Martin Hering-Bertram ◽  
Lars Linsen
Keyword(s):  
Large Data ◽  
Automatic Identification ◽  
Identification System ◽  
Very High Frequency ◽  
Database Queries ◽  
Related Data ◽  
Movement Prediction ◽  
Time Period ◽  
Current Usage ◽  
Exchange Vessel

AIS was primarily developed to exchange vessel-related data among vessels or AIS stations by using very-high frequency (VHF) technology to increase safety at sea. This study evaluates the formal integrity, availability, and the reporting intervals of AIS data with a focus on vessel movement prediction. In contrast to former studies, this study is based on a large data collection of over 85 million AIS messages, which were continuously received within a time period of two months. Thus, the evaluated data represent a comprehensive and up-to-date view of the current usage of AIS systems installed on vessels. Results of previous studies concerning the availability of AIS data are confirmed and extended. New aspects such as reporting intervals are additionally evaluated. Received messages are stored in a database, which allows for performing database queries to evaluate the obtained data in an automatic way. This study shows that almost ten years after becoming mandatory for professional operating vessels, AIS still lacks availability for both static and dynamic data and that the reporting intervals are not as reliable as specified within the technical AIS standard.

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