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According to the Global Maritime Insurance annual report, among human and non-human risk factors, the number of accidents in maritime transport remains a significant issue. One of the factors is vessel collisions and anomalies at sea. Massive historical data from automatic identification systems are analyzed, and intelligent transportation systems are being developed to solve the problem of vessel trajectory prediction. The most ordinary attempt to improve accuracy is by evaluating the historical vessel behavior and learning the patterns and similarities of the predicted vessel movements. However, this paper shows that a better forecast also may be reached by choosing a different trajectory calculation strategy. The geographical or polar coordinate system values are used in a classical way, but several modifications, such as Universal Transverse Mercator (UTM), have been proposed in this study as an alternative to Mercator's projection coordinates. Two main positioning of the vessels motion transformations were tested: by changing the coordinates to measurements of the angular distance (haversine) and displacement angle (azimuth) functions between different time steps; degree coordinates transformation into a Cartesian system using UTM with vector subtraction. The last case improves the accuracy of almost 30% in the available data sample by using the autoencoder architecture, compared to the longitude and latitude predictions even with computed delta features. The research generally compared three recurrent network architectures (with their hyperparameter – cell sizes): Autoencoder Long Short-Term Memory, Bi-directional Long Short-Term Memory, and Gated Recurrent Unit networks. The model calculations are performed in a real historical dataset, exclusively on cargo vessel type trajectories in the Netherlands (North Sea) coastal region. Also, the methods were validated in another dataset of the Baltic Sea Region. • The accuracy of the prediction begins to improve with a higher number of LSTM cells. • A recursive multi-step model in Cartesian system yields notably better results. • The autoencoder architecture distinguish more stable standard deviation value errors. [ABSTRACT FROM AUTHOR]