Predicting Vessel Trajectories from Ais Data Using R

Abstract

Analysts and security experts seek automated algorithms to predict future behavior of vessels at sea based on Automated Identification System (AIS) data. This thesis seeks to accurately predict the future location of a vessel at sea based on cluster analysis of historical vessel trajectories using a random forest. Once similar trajectories have been clustered into a route, expected prediction error can be empirically estimated based on an independent validation data set not used during training, then applied to an independent test set to produce an expected prediction region with a user-defined level of expectation. Our results show that the prediction region contains the true interpolated future position at the expectation level set by the user, therefore producing a valid methodology for both estimating the future vessel location and for assessing anomalous vessel behavior.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2017
Accession Number
AD1046595

Entities

People

  • Brian L. Young

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Aircrafts
  • Anomaly Detection
  • Artificial Intelligence
  • Change Detection
  • Coast Guard
  • Computational Science
  • Data Mining
  • Data Sets
  • Geographic Regions
  • Identification Systems
  • Information Science
  • Information Systems
  • Maritime Domain Awareness
  • Neural Networks
  • Operations Research
  • Test Sets
  • Training

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Oceanography.