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

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

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.

Suggested Citation

  • Zhao, Jiansen & Lu, Jinquan & Chen, Xinqiang & Yan, Zhongwei & Yan, Ying & Sun, Yang, 2022. "High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    • Handle: RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007433
    DOI: 10.1016/j.physa.2021.126470
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    References listed on IDEAS

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    1. Wu, Bing & Tang, Yuheng & Yan, Xinping & Guedes Soares, Carlos, 2021. "Bayesian Network modelling for safety management of electric vehicles transported in RoPax ships," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Du, Lei & Goerlandt, Floris & Kujala, Pentti, 2020. "Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    3. Bye, Rolf J. & Aalberg, Asbjørn L., 2018. "Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 174-186.
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    Citations

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    Cited by:

    1. Wang, Yukuan & Liu, Jingxian & Liu, Ryan Wen & Wu, Weihuang & Liu, Yang, 2023. "Interval prediction of vessel trajectory based on lower and upper bound estimation and attention-modified LSTM with bayesian optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    2. Wang, Yinpu & An, Chengchuan & Ou, Jishun & Lu, Zhenbo & Xia, Jingxin, 2022. "A general dynamic sequential learning framework for vehicle trajectory reconstruction using automatic vehicle location or identification data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    3. Gao, Dawei & Zhu, Yongsheng & Guedes Soares, C., 2023. "Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate Gaussian Process," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Zhao, Jiansen & Yan, Zhongwei & Chen, Xinqiang & Han, Bing & Wu, Shubo & Ke, Ranxuan, 2022. "k-GCN-LSTM: A k-hop Graph Convolutional Network and Long–Short-Term Memory for ship speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    5. Philip Cammin & Jingjing Yu & Stefan Voß, 2023. "Tiered prediction models for port vessel emissions inventories," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 142-169, March.

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