首页|High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework
High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework
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NSTL
Elsevier
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. (C) 2021 Elsevier B.V. All rights reserved.
High-fidelity ship trajectoryEmpirical mode decompositionArtificial neural networkMaritime traffic managementAIS DATA
NSTL
Elsevier
