Development of a method for predicting hazardous ship trajectories under uncertainty of navigator actions

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The object of the research is the automation processes in maritime navigation to ensure the safety of ship movement by predicting their trajectories in complex aquatic areas, such as narrow passages, straits, and ports. The research applied six key stages to create a comprehensive method for clustering and predicting ship trajectories based on ECDIS data. In the first stage, ship movement trajectories were constructed according to risk categories, using the LCSS and DTW algorithms to compare planned and actual trajectories. This allowed for the accurate identification of course deviations and the determination of potentially dangerous sections of the trajectory. The second stage implemented clustering using the DBSCAN and GMM algorithms. DBSCAN was used to identify the density of points in space, and GMM provided modeling of cluster probabilities, allowing for better risk zone determination. The third stage applied the Douglas-Peucker compression algorithm to reduce the number of points in the trajectories, which preserved key characteristics and optimized data processing. In the fourth stage, ship movement stability was assessed using the Fourier transform, which allowed the detection of high-frequency oscillations that may indicate movement instability caused by changes in course or speed. The fifth stage included fuzzy clustering of trajectories using the Gaussian Mixture Model (GMM), which allowed modeling the probabilities of dangerous trajectories, considering the uncertainty of navigational parameters. At the final stage, a multilayer neural network (MLP) was used to predict future points of ship trajectories. The model accurately predicted the ship's coordinates, enabling timely trajectory adjustments. Experimental results showed that the developed method increased the accuracy of ship trajectory prediction to 72–81 % and also significantly reduced the final error, ensuring effective risk management during complex navigation.