Short-term vessel trajectory and manoeuvre prediction

Short-term vessel trajectory and manoeuvre prediction: Predicting vessel trajectories including swing manoeuvres in a scoped area of the Port of Antwerp-Bruges

van Loon, Joaquin (TU Delft Electrical Engineering, Mathematics and Computer Science)

Yorke-Smith, N. (mentor)
Rajan, R.T. (graduation committee)
Koppenberg, Timo (graduation committee)

Delft University of Technology

Computer Science

2023-06-14

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.

Vessel manoeuvre
Trajectory prediction
Swing manoeuvres
Deep Learning
Optimising ports
Congestion
Collision avoidance
Vessel motions
Vessel Traffic Management

http://resolver.tudelft.nl/uuid:87d2e07f-2892-4e75-b730-b4d0800c80e2

Student theses

master thesis

© 2023 Joaquin van Loon