Work

A code distribution-based trajectory representation and its application to similarity, clustering and classification of AIS big data

Year: 2026

Type: article

Abstract: Automatic Identification System (AIS) data received from vessels in a maritime area of interest is a valuable resource for understanding vessel behavior and gaining insights into maritime activities. This paper presents a novel approach for representing vessel trajectories using code distribution and analyzing AIS trajectory big data through machine learning techniques. By introducing PQk-means vector quantization algorithms, AIS trajectory data records are transformed into a series of code documents. Applying the TF-IDF (Term Frequency-Inverse Document Frequency) technique from text mining to these code documents produces a code distribution-based representation of vessel trajectories. This preliminary process enables the application of machine learning algorithms to AIS trajectory big data. Using this representation, three types of applications have been developed: detecting similar trajectories and vessels using vector space models and cosine similarity, clustering voyages and vessels with the K-means algorithm, and recognizing vessels with support vector machine algorithms. The potential of the proposed approach is demonstrated through a series of experiments using practical AIS datasets from a region in northwest France. Overall, the experimental results show that the proposed approach is highly effective for mining AIS big data, outperforms other methods, and confirms its ability to handle high-dimensional trajectories and massive amounts of AIS data within a reasonable computational cost. Moreover, this work provides an opportunity to develop an AIS-oriented version of a large language model based on our code distribution representation of trajectories, and to extend trajectory representation to any type of moving object or numerical vector from diverse sensors.

Source: Expert Systems with Applications

DOI: 10.1016/j.eswa.2026.131216

Authors: Iwao Fujino, Clément Iphar, Christophe Claramunt