Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2025

Abstract

Recognizing the specific complexities of vessel traffic flow, this comprehensive survey exclusively addresses the predictive modelling in maritime transportation, tracing the evolution from conventional statistical approaches to modern artificial intelligence (AI) techniques. The survey examines a broad range of predictive targets, including vessel volume, trajectories, velocities, destinations and traffic patterns. Through bibliometric analysis utilizing Citespace, the central research themes and technological trends characterizing the vessel traffic flow prediction domain have been identified and discussed. Our analysis indicates a clear trend towards AI-based models, highlighting their increasing dominance in enhancing predictive accuracy and efficiency. Additionally, we highlight persistent challenges, such as the integration of large datasets with traffic flow models and the critical need for real-time data analytics. The survey concludes with insights into the future of vessel traffic flow prediction research, emphasizing the potential of hybrid models that combine deep learning with statistical learning to enable more sophisticated predictive analytics to be performed. This review aims to serve as a guide for both academics and practitioners looking to maximize the use of predictive modelling in the maritime traffic sector.

Keywords

Vessel traffic flow prediction, Predictive modelling, Artificial intelligence, External information, Predictive duration, Modelapplicability

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Transportation Safety and Environment

Volume

7

Issue

3

First Page

1

Last Page

21

Identifier

10.1093/tse/tdaf022

Publisher

Oxford University Press

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