IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v586y2022ics0378437121007433.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121007433
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126470?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cai, Mingyou & Zhang, Jinfen & Zhang, Di & Yuan, Xiaoli & Soares, C. Guedes, 2021. "Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Zhang, Weibin & Feng, Xinyu & Goerlandt, Floris & Liu, Qing, 2020. "Towards a Convolutional Neural Network model for classifying regional ship collision risk levels for waterway risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    3. Silveira, P. & Teixeira, A.P. & Figueira, J.R. & Guedes Soares, C., 2021. "A multicriteria outranking approach for ship collision risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    4. Gil, Mateusz & Kozioł, Paweł & Wróbel, Krzysztof & Montewka, Jakub, 2022. "Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    5. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2021. "Spatial correlation analysis of near ship collision hotspots with local maritime traffic characteristics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Yang, Zaili, 2023. "Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Zhang, Mingyang & Montewka, Jakub & Manderbacka, Teemu & Kujala, Pentti & Hirdaris, Spyros, 2021. "A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    8. Murray, Brian & Perera, Lokukaluge Prasad, 2021. "An AIS-based deep learning framework for regional ship behavior prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Montewka, Jakub & Manderbacka, Teemu & Ruponen, Pekka & Tompuri, Markus & Gil, Mateusz & Hirdaris, Spyros, 2022. "Accident susceptibility index for a passenger ship-a framework and case study," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    10. Xin, Xuri & Liu, Kezhong & Yang, Zaili & Zhang, Jinfen & Wu, Xiaolie, 2021. "A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Zhang, Yang & Sun, Xukai & Chen, Jihong & Cheng, Cheng, 2021. "Spatial patterns and characteristics of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    13. Vytautas Paulauskas & Viktoras Senčila & Donatas Paulauskas & Martynas Simutis, 2023. "Impact of Port Shallowness (Clearance under the Ship’s Keel) on Shipping Safety, Energy Consumption and Sustainability of Green Ports," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    14. Adland, Roar & Jia, Haiying & Lode, Tønnes & Skontorp, Jørgen, 2021. "The value of meteorological data in marine risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    15. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
    16. Munim, Ziaul Haque & Sørli, Michael André & Kim, Hyungju & Alon, Ilan, 2024. "Predicting maritime accident risk using Automated Machine Learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    17. Wang, Xinjian & Liu, Zhengjiang & Loughney, Sean & Yang, Zaili & Wang, Yanfu & Wang, Jin, 2022. "Numerical analysis and staircase layout optimisation for a Ro-Ro passenger ship during emergency evacuation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    18. Puisa, Romanas & Montewka, Jakub & Krata, Przemyslaw, 2023. "A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    19. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    20. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.