Short-term vessel trajectory and manoeuvre prediction

Predicting vessel trajectories including swing manoeuvres in a scoped area of the Port of Antwerp-Bruges

Master thesis (2023)

Authors

J.L.K. van Loon Electrical Engineering, Mathematics and Computer Science

Contributors

N. Yorke-Smith Algorithmics - EEMCS (supervisor 1)
R.T. Rajan Signal Processing Systems - EEMCS (supervisor 2)
Timo Koppenberg Macomi B.V. (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Joaquin van Loon
Deep Learning Collision avoidance Trajectory prediction Congestion Vessel motions Vessel Traffic Management Vessel manoeuvre Swing manoeuvres Optimising ports
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Published Date

14-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Situational awareness within port areas is crucial to avoid collisions, navigate efficiently and reduce congestion. Maritime-traffic controllers constantly monitor the situation in the port and intervene when needed. This study proposes a deep learning model that predicts future vessel positions to assist in this process. The model employs a target conditioned trajectory prediction component composed of two recurrent neural networks arranged in an encoder-decoder structure that utilizes historical data points to forecast future trajectories. The model considers multiple factors, including vessel speed, location, length, depth, draught, and the tide. Additionally, this study addresses the prediction of swing manoeuvres, which are special U-turn-like manoeuvres executed during terminal arrival or departure. These manoeuvres can block a significant portion of the waterway and, as such, are essential to consider when gaining a complete understanding of future situations within the port. An integration of both models is applied to a use case study in a scoped area of the Port of Antwerp-Bruges. The models were
trained using AIS and VTS data collected at 30-second intervals. Swing manoeuvres are predicted with an accuracy of 84%, the locations of these manoeuvres are predicted with an average deviation of 212 meter and the duration error is 1.6 minutes on average. The complete predicted trajectories, including potential swing manoeuvres, have an average displacement error and final displacement error of 147 and 117 meter on average, respectively. Overall, the study demonstrates the potential of deep learning models for improving situational awareness within port areas and assisting traffic controllers in making
informed decisions.