Vessel Trajectory Deviation Detection Using a Temporal-Attention LSTM Autoencoder

Abstract

Abnormal vessel trajectories are critical indicators of potential collision risks, illegal navigation behaviors, and unexpected route deviations. This study uses labelled Automatic Identification System (AIS) trajectories from Danish waters to examine a Long Short-Term Memory (LSTM) autoencoder with temporal attention for vessel trajectory deviation detection. Latitude, longitude, speed, and course are processed as fixed-length sequences, and the model is trained only with normal trajectories. Reconstruction error is then used as the anomaly score. The model is compared with Isolation Forest, a dense autoencoder, a basic LSTM autoencoder, and a position-only LSTM model under the same Maritime Mobile Service Identity (MMSI)-based data split. Multi-seed repeated experiments over five independent runs show that the attention LSTM obtains a mean receiver operating characteristic area under the curve (ROC-AUC) of 0.859, recall of 0.725, and F1-score of 0.724. The improvement over the basic LSTM is small, while the dense autoencoder gives a slightly higher average precision. The results also show that speed and course are important for detecting abnormal movement. A sequence length and hidden size of 32, together with the 95th-percentile threshold, give the most balanced results. The dataset contains few abnormal samples, so further testing on larger datasets and different sea areas is still needed.