Investigations of Anomalies in Ship Movement During a Voyage

This study proposes a method for identifying anomalous ship behavior using AIS data and prediction-error analysis based on a Long Short-Term Memory (LSTM) neural network. The approach compares predicted and observed positions, courses, and speeds to detect significant deviations indicative of abnormal maneuvers, route changes, or intentional AIS manipulation. A trajectory prediction model was trained on historical AIS streams and evaluated using independent test vessels. Quantitative criteria and threshold values for anomaly detection were derived from navigational standards, AIS accuracy characteristics, and empirical sensitivity analysis. The method was validated on voyages between the North Sea and the Baltic Sea, demonstrating its capability to highlight potentially unsafe or suspicious vessel behavior. The results show that combining machine-learning-based prediction and multi-parameter deviation analysis can improve automated maritime surveillance and support operational decision-making.