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Vessel Trajectory Prediction Using Historical Automatic Identification System Data

Published online by Cambridge University Press:  26 August 2020

Danial Alizadeh ,
Ali Asghar Alesheikh Open the ORCID record for Ali Asghar Alesheikh [Opens in a new window]  and
Mohammad Sharif Open the ORCID record for Mohammad Sharif [Opens in a new window]
Danial Alizadeh
Affiliation:
(Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran)
Ali Asghar Alesheikh
Affiliation:
(Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran)
Mohammad Sharif*
Affiliation:
(Department of Geography, Faculty of Humanities, University of Hormozgan, Bandar Abbas, Iran)
*
(E-mail: m.sharif@hormozgan.ac.ir)
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Abstract

For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40·85% for the second model compared with the first model and 23% for the third model compared with the second model.

Keywords

TrajectoryMovement ModelsShip BehaviourAutomatic Identification System
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 1 , January 2021 , pp. 156 - 174
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Copyright
Copyright © The Royal Institute of Navigation 2020

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