Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction

@article{Pallotta2013VesselPK,
  title={Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction},
  author={Giuliana Pallotta and Michele Vespe and Karna Bryan},
  journal={Entropy},
  year={2013},
  volume={15},
  pages={2218-2245}
}
Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of… 
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421 Citations
Highly Influential Citations
32
Background Citations
126
Methods Citations
103
Results Citations
5

421 Citations

Citation Type

Citation Type

Traffic knowledge discovery from AIS data
TLDR
This paper proposes an unsupervised and incremental learning approach to extract the historical traffic patterns from AIS data to provide operators with a configurable knowledge framework supporting day by day decision making and general awareness of vessel pattern of life activity.
Automatic Maritime Traffic Synthetic Route: A Framework for Route Prediction
TLDR
This paper proposes a method to calculate route prediction from a synthetic route representation data once the picture of the maritime traffic is constructed and shows a good tradeoff between the predicted and the actual observed vessel routes.
Data-driven detection and context-based classification of maritime anomalies
TLDR
A data-driven approach to anomaly detection, highlighting challenges specific to the maritime domain is proposed and builds on unsupervised learning techniques which provide models for normal traffic behaviour.
A distributed framework for extracting maritime traffic patterns
TLDR
The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers, showing that the biggest part of any future vessel trajectory falls within the extracted shipping lanes.
Extracting Courses of Vessels from AIS Data and Real-Time Warning Against Off-Course
TLDR
It appears, that from machine learning and natural language processing principles, a topic model might provide a novel approach for extracting implicit patterns underlying massive and unstructured collection of incoming data considered as documents.
Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection
TLDR
This article extends an existing network abstraction of maritime traffic that is based on nodes that correspond to naval areas of long stays or major turns for vessels and enriches them with semantic information about the different ways that vessels employ when traversing an edge, and proposes an alternative of the popular density based clustering algorithm DB-Scan.
Data mining approach to shipping route characterization and anomaly detection based on AIS data
Trajectory pattern extraction and anomaly detection for maritime vessels
Discovering Maritime Traffic Route from AIS network
TLDR
The proposed framework of Maritime Traffic Route Discovery is evaluated on real AIS data and the experimental results shows that the proposed MTRD is able to extract the marine traffic route effectively and provides a cornerstone of maritime traffic knowledge for traffic management, anomaly detection, and conflict analysis in the future.
Local Anomaly Detection In Maritime Traffic Using Visual Analytics
TLDR
This work proposes a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them, and displays these scores in a tabular visualization where users can rank trips by segment to find local anomalies.
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References

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