Characterizing ship navigation patterns using Automatic Identification System (AIS) data in the Baltic Sea
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Authors
von Eiff, Janet S.
Subjects
AIS
Baltic Sea
data analysis
regression
higher-order network
Bayes information criterion
random forest
Baltic Sea
data analysis
regression
higher-order network
Bayes information criterion
random forest
Advisors
Koyak, Robert A.
Date of Issue
2018-03
Date
Mar-18
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
The Intelligence, Surveillance, and Reconnaissance (ISR) community is interested in developing a model that can assist in characterizing patterns of ship navigation. We examine techniques used to highlight those patterns using historical Automatic Identification System (AIS) data in the Baltic Sea from January to April 2014. A regression model is used to determine which factors influence the amount of time a cargo ship spends in a port in the Saint Petersburg, Russia, area. We find that the best model is able to explain about 29 percent of the variance of the length of time that a vessel is in the Saint Petersburg area. We use three random forest models, that differ in their use of past information, to predict a vessel’s next port of visit. The random forest models we use in this analysis demonstrate that predicting a vessel’s next port of call is not a Markov model but a higher-order network where past information is used to more accurately predict the future state. The transitional probabilities change when predictor variables are added that reach deeper into the past. Our findings suggest that successful prediction of the movement of a vessel depends on having accurate information on its recent history.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
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NPS Report Number
Sponsors
Funder
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.