Next Article in Journal
Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems
Previous Article in Journal
Entropy Harvesting
Article Menu

Export Article

Open AccessArticle
Entropy 2013, 15(6), 2218-2245; doi:10.3390/e15062218

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

NATO Science and Technology Organization (STO), Centre for Maritime Research and Experimentation (CMRE), Viale San Bartolomeo 400, 19126, La Spezia, Italy
*
Author to whom correspondence should be addressed.
Received: 1 March 2013 / Revised: 10 May 2013 / Accepted: 29 May 2013 / Published: 4 June 2013
View Full-Text   |   Download PDF [3845 KB, uploaded 24 February 2015]   |  

Abstract

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 automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (i.e., sensor coverage and performance), persistence (i.e., time lag between subsequent observations) and data sources (i.e., ground-based and space-based receivers). View Full-Text
Keywords: maritime situational awareness; knowledge discovery; maritime route extraction; route prediction; anomaly detection maritime situational awareness; knowledge discovery; maritime route extraction; route prediction; anomaly detection
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Pallotta, G.; Vespe, M.; Bryan, K. Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction. Entropy 2013, 15, 2218-2245.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top