IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v230y2023ics0951832022005348.html
   My bibliography  Save this article

An interpretable knowledge-based decision support method for ship collision avoidance using AIS data

Author

Listed:
  • Zhang, Jinfen
  • Liu, Jiongjiong
  • Hirdaris, Spyros
  • Zhang, Mingyang
  • Tian, Wuliu

Abstract

AIS data include ship spatial-temporal and motion parameters which can be used to excavate the deep-seated information. In this article, an interpretable knowledge-based decision support method is established to guide the ship to make collision avoidance decisions with good seamanship and ordinary practice of seamen using AIS data. First, AIS data is preprocessed and trajectory reconstructed to restore the ship historical navigation state, and a ship encounter identification model is constructed according to the encounter characteristics; Second, a two-stage collision avoidance behavior extraction algorithm is formed to build a behavior knowledge base, and the scenario similarity model is constructed to measure and match similar scenarios based on ship position, motion tendency and collision risk. Then, the Delaunay Triangulation Network is used to fuse ship trajectories of similar scenario to form the collision avoidance path. Finally, a case study is performed using the real AIS data outside Ningbo-Zhoushan Port waters, China, and the effectiveness of the planned path is verified by setting the head-on and crossing situations and comparison between the planned and real paths. Results indicate that the proposed model can extract the ship collision avoidance behavior accurately, and the planned path can ensure navigation safety.

Suggested Citation

  • Zhang, Jinfen & Liu, Jiongjiong & Hirdaris, Spyros & Zhang, Mingyang & Tian, Wuliu, 2023. "An interpretable knowledge-based decision support method for ship collision avoidance using AIS data," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005348
    DOI: 10.1016/j.ress.2022.108919
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022005348
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108919?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. 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).
    2. Du, Lei & Banda, Osiris A. Valdez & Huang, Yamin & Goerlandt, Floris & Kujala, Pentti & Zhang, Weibin, 2021. "An empirical ship domain based on evasive maneuver and perceived collision risk," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Dong Yang & Lingxiao Wu & Shuaian Wang & Haiying Jia & Kevin X. Li, 2019. "How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications," Transport Reviews, Taylor & Francis Journals, vol. 39(6), pages 755-773, November.
    4. 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).
    5. Zhang, Liye & Meng, Qiang & Fang Fwa, Tien, 2019. "Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 287-304.
    6. Zhang, Mingyang & Kujala, Pentti & Hirdaris, Spyros, 2022. "A machine learning method for the evaluation of ship grounding risk in real operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Kujala, P. & Hänninen, M. & Arola, T. & Ylitalo, J., 2009. "Analysis of the marine traffic safety in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1349-1357.
    8. Bertolazzi, Enrico & Frego, Marco & Biral, Francesco, 2020. "Point data reconstruction and smoothing using cubic splines and clusterization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 176(C), pages 36-56.
    9. M B Zaman & E Kobayashi & N Wakabayashi & S Khanfir & T Pitana & A Maimun, 2014. "Fuzzy FMEA model for risk evaluation of ship collisions in the Malacca Strait: based on AIS data," Journal of Simulation, Taylor & Francis Journals, vol. 8(1), pages 91-104, February.
    10. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    11. Ziaul Haque Munim & Mariia Dushenko & Veronica Jaramillo Jimenez & Mohammad Hassan Shakil & Marius Imset, 2020. "Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 577-597, July.
    12. 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).
    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. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Li, Huanhuan & Ekere, Nduka & Yang, Zaili, 2023. "Multi-scale collision risk estimation for maritime traffic in complex port waters," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    2. 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).
    3. Wang, Yang & Ye, Ting & Zio, Enrico & Wang, Tengfei & Wu, Bing, 2024. "A blockchain-based credibility evaluation scheme for navigational event dissemination in the internet of ships," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    4. Yang, Ying & Liu, Yang & Li, Guorong & Zhang, Zekun & Liu, Yanbin, 2024. "Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).

    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. 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).
    2. Mazurek, J. & Lu, L. & Krata, P. & Montewka, J. & Krata, H. & Kujala, P., 2022. "An updated method identifying collision-prone locations for ships. A case study for oil tankers navigating in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. 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).
    4. Zhang, Mingyang & Kujala, Pentti & Hirdaris, Spyros, 2022. "A machine learning method for the evaluation of ship grounding risk in real operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Li, Huanhuan & Ekere, Nduka & Yang, Zaili, 2023. "Multi-scale collision risk estimation for maritime traffic in complex port waters," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    6. Gao, Dawei & Zhu, Yongsheng & Yan, Ke & Soares, C. Guedes, 2024. "Deep learning–based framework for regional risk assessment in a multi–ship encounter situation based on the transformer network," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. 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).
    8. 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).
    9. Mauro, Francesco & Vassalos, Dracos & Paterson, Donald, 2022. "Critical damages identification in a multi-level damage stability assessment framework for passenger ships," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    10. Ruponen, Pekka & Montewka, Jakub & Tompuri, Markus & Manderbacka, Teemu & Hirdaris, Spyros, 2022. "A framework for onboard assessment and monitoring of flooding risk due to open watertight doors for passenger ships," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    11. Zvyagina, Tatiana & Zvyagin, Petr, 2022. "A model of multi-objective route optimization for a vessel in drifting ice," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    12. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    13. Yang, Ying & Liu, Yang & Li, Guorong & Zhang, Zekun & Liu, Yanbin, 2024. "Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    14. Li, Mengxia & Mou, Junmin & Chen, Pengfei & Rong, Hao & Chen, Linying & van Gelder, P.H.A.J.M., 2022. "Towards real-time ship collision risk analysis: An improved R-TCR model considering target ship motion uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    15. Carine Dominguez-Péry & Lakshmi Narasimha Raju Vuddaraju & Isabelle Corbett-Etchevers & Rana Tassabehji, 2021. "Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda," Journal of Shipping and Trade, Springer, vol. 6(1), pages 1-32, December.
    16. Cheng, Tingting & Veitch, Erik A. & Utne, Ingrid Bouwer & Ramos, Marilia A. & Mosleh, Ali & Alsos, Ole Andreas & Wu, Bing, 2024. "Analysis of human errors in human-autonomy collaboration in autonomous ships operations through shore control experimental data," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    17. Sugrue, Dennis & Adriaens, Peter, 2021. "A data fusion approach to predict shipping efficiency for bulk carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    18. 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).
    19. Fu, Shanshan & Yu, Yuerong & Chen, Jihong & Xi, Yongtao & Zhang, Mingyang, 2022. "A framework for quantitative analysis of the causation of grounding accidents in arctic shipping," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    20. Zyczkowski, Marcin & Szlapczynski, Rafal, 2023. "Collision risk-informed weather routing for sailboats," Reliability Engineering and System Safety, Elsevier, vol. 232(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:reensy:v:230:y:2023:i:c:s0951832022005348. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.