Highlights Conduct the state-of-the-art survey to generate twelve advanced prediction methods. Carry out the comprehensive experimental comparison and deep analysis. Develop a benchmark for trajectory prediction methods in maritime traffic. Employ six evaluation indexes to assess the performance of twelve prediction methods. Evaluate the fitness of the twelve prediction methods against different scenarios. Validate their performance in three complex water areas in different scenarios.
Abstract Maritime transport faces new safety challenges in an increasingly complex traffic environment caused by large-scale and high-speed ships, particularly with the introduction of intelligent and autonomous ships. It is evident that Automatic Identification System (AIS) data-driven ship trajectory prediction can effectively aid in identifying abnormal ship behaviours and reducing maritime risks such as collision, stranding, and contact. Furthermore, trajectory prediction is widely recognised as one of the critical technologies for realising safe autonomous navigation. The prediction methods and their performance are the key factors for future safe and automatic shipping. Currently, ship trajectory prediction lacks the real performance measurement and analysis of different algorithms, including classical machine learning and emerging deep learning methods. This paper aims to systematically analyse the performance of ship trajectory prediction methods and pioneer experimental tests to reveal their advantages and disadvantages as well as fitness in different scenarios involving complicated systems. To do so, five machine learning methods (i.e., Kalman Filter (KF), Support Vector Progression (SVR), Back Propagation network (BP), Gaussian Process Regression (GPR), and Random Forest (RF)) and seven deep learning methods (i.e., Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (Bi-LSTM), Sequence to Sequence (Seq2seq), Bi-directional Gate Recurrent Unit (Bi-GRU), and Transformer) are first extracted from the state-of-the-art literature review and then employed to implement the trajectory prediction and compare their prediction performance in the real world. Three AIS datasets are collected from the waters of representative traffic features, including a normal channel (i.e., the Chengshan Jiao Promontory), complex traffic (i.e., the Zhoushan Archipelago), and a port area (i.e., Caofeidian port). They are selected to test and analyse the performance of all twelve methods based on six evaluation indexes and explore the characteristics and effectiveness of the twelve trajectory prediction methods in detail. The experimental results provide a novel perspective, comparison, and benchmark for ship trajectory prediction research, which not only demonstrates the fitness of each method in different maritime traffic scenarios, but also makes significant contributions to maritime safety and autonomous shipping development.
AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods
2023-05-08
Article (Journal)
Electronic Resource
English
Review: Ship trajectory prediction based on AIS data using machine learning methods
ORKG Comparisons | 2023
|Deep learning-based civil ship trajectory behavior analysis optimization method
European Patent Office | 2023
|Trajectory Data-Driven Network Representation for Traffic State Prediction using Deep Learning
Springer Verlag | 2024
|Trajectory Prediction of Vehicles Based on Deep Learning
IEEE | 2019
|