High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework

Jiansen Zhao, Jinquan Lu, Xinqiang Chen, Zhongwei Yan, Ying Yan and Yang Sun

Physica A: Statistical Mechanics and its Applications, 2022, vol. 586, issue C

Abstract: Ship trajectory from automatic identification system (AIS) provides crucial kinematic information for various maritime traffic participants (ship crew, maritime officials, shipping company, etc.), which greatly benefits the maritime traffic management in real-world. In that manner, ship trajectory smoothing and prediction attracts significant attentions in the maritime traffic community. To address the issue, an ensemble machine learning framework is proposed to remove outliers in the raw AIS data and predict ship trajectory variation tendency. Our method is verified on three typical ship trajectory segments, which is compared against other ship trajectory prediction models. The experimental results suggested that our proposed framework obtained higher prediction accuracy compared to the common trajectory prediction models in terms of typical error measurement indicators. The research findings can help maritime traffic participants obtain high-fidelity ship trajectory data, which supports making more reasonable traffic-controlling decisions.

Keywords: High-fidelity ship trajectory; Empirical mode decomposition; Artificial neural network; Maritime traffic management (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007433

DOI: 10.1016/j.physa.2021.126470

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