Enhancing short-term vessel trajectory prediction with clustering for heterogeneous and multi-modal movement patterns.

Predicting vessel trajectories is crucial for enhancing situational awareness and preventing collisions at sea. However, achieving accurate and efficient predictions is challenging due to the heterogeneity in vessel movement patterns and changes in vessel mobility modes during voyages. To address this, we propose a new approach that uses historical AIS data to cluster route patterns for each vessel type, thereby improving prediction accuracy. By training machine learning algorithms to focus only on similar vessel types, this approach can better predict individual vessel mobility patterns. This approach offers computational advantages by using a relatively small set of trajectories from the nearest cluster of a selected vessel. Both spatial and course attributes are considered to determine the nearest cluster, while engineered features capture changes in vessel mobility modes. Using an AIS dataset from UTM Zone 10N (US West Coast), we achieved distance errors of 370 m , 742 m , and 1. 2 k m for horizons 10, 20, and 30 min, respectively, using the Random Forest algorithm for short-term trajectory prediction (≤ 30 min) with the last 1-hour trajectory of selected vessels as input. • Proposed framework for predicting the short-term future trajectory of vessels. • Enhanced prediction accuracy by addressing heterogeneity in mobility patterns. • Engineered features are incorporated to consider changes in mobility modes. • Spatial and course attributes are used for trajectory classification. • Evaluation shows high prediction accuracy with low computational cost. [ABSTRACT FROM AUTHOR]