Vessel Trajectory Prediction in Maritime Transportation: Current Approaches and Beyond

被引：84

作者：

Zhang, Xiaocai ^{[1
]}

Fu, Xiuju ^{[1
]}

Xiao, Zhe ^{[1
]}

Xu, Haiyan ^{[1
]}

Qin, Zheng ^{[1
]}

机构：

[1] Inst High Performance Comp, A STAR, Singapore 138632, Singapore

来源：

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2022年 / 23卷 / 11期

关键词：

Trajectory; Transportation; Safety; Predictive models; Hidden Markov models; Support vector machines; Soft sensors; Maritime transportation; safety; vessel; trajectory prediction; deep learning; AIS DATA; NETWORK; MODEL; REGRESSION; SELECTION; LSTM;

D O I：

10.1109/TITS.2022.3192574

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

The growing availability of maritime IoT traffic data and continuous expansion of the maritime traffic volume, serving as the driving fuel, propel the latest Artificial Intelligence (AI) studies in the maritime domain. Among the most recent advancements, vessel trajectory prediction is one of the most essential topics for assuring maritime transportation safety, intelligence, and efficiency. This paper presents an up-to-date review of existing approaches, including state-of-the-art deep learning, for vessel trajectory prediction. We provide a detailed explanation of data sources and methodologies used in the vessel trajectory prediction studies, highlight a discussion regarding the auxiliary techniques, complexity analysis, benchmarking, performance evaluation, and performance improvement for vessel trajectory prediction research, and finally summarize the current challenges and future research directions in this field.

引用

页码：19980 / 19998

页数：19

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