Long-Term Trajectory Prediction for Oil Tankers via Grid-Based Clustering

Abstract

Vessel trajectory prediction is an important step in route planning, which could help improve the efficiency of maritime transportation. In this article, a high-accuracy long-term trajectory prediction algorithm is proposed for oil tankers. The proposed algorithm extracts a set of waymark points that are representative of the key traveling patterns in an area of interest by applying DBSCAN clustering to historical AIS data. A novel path-finding algorithm is then developed to sequentially identify a subset of waymark points, from which the predicted trajectory to a fixed destination is produced. The proposed algorithm is tested using real data offered by the Danish Maritime Authority. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art vessel trajectory prediction algorithms and is able to make high-accuracy long-term trajectory predictions.

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

“…Traditional methods mainly include simulation, statistical, knowledge-based, and control theorybased methods. For example, Xu et al [20] designed a high-precision, long-period oil tanker trajectory prediction algorithm, which applies the density-based spatial clustering of applications with noise (DBSCAN) clustering to process AIS data, as well as extracts a series of key points representing critical navigation modes. They then developed, in turn, a novel path search algorithm to select one part of these key points to generate a predicted trajectory to a fixed target.…”

“…Existing methods for predicting vessel trajectory fall into three categories: traditional [13,14], machine learning [15,16], and deep learning [17][18][19]. Traditional approaches primarily rely on empirical and mathematical models following specific physical laws [20,21]. However, the application scenarios of traditional methods depend on boundary conditions.…”

The Identification of Ship Trajectories Using Multi-Attribute Compression and Similarity Metrics

“…Wei et al (2023) [13] proposed a time-varying ensemble model utilizing feature selection and clustering techniques to enhance the real-time prediction of ship motion performance. Xu et al (2023) [14] used the key point clustering to extract travel patterns of tankers in a designated area of interest from historical AIS data. Mou et al (2018) [15] enhanced the conventional Hausdorff distance by replacing the scale parameter with the ship trajectory's mean distance, took the entire ship trajectories as the clustering unit, and acquired the clustering outcomes for the estuarine waters of the Yangtze River.…”