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Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

Author

Listed:
  • Xuyang Han

    (Geomatics Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada)

  • Costas Armenakis

    (Geomatics Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada)

  • Mojgan Jadidi

    (Geomatics Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada)

Abstract

Today, maritime transportation represents a substantial portion of international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this paper, we present an enhanced density-based spatial clustering of applications with noise (DBSCAN) method to model vessel behaviours based on trajectory point data. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis distance metric, which considers the correlation between the points representing vessel locations. This research proposes applying the clustering method to historical Automatic Identification System (AIS) data using an algorithm to generate a clustering model of the vessels’ trajectories and a model for detecting vessel trajectory anomalies, such as unexpected stops, deviations from regulated routes, or inconsistent speed. Further, an automatic and data-driven approach is proposed to select the initial parameters for the enhanced DBSCAN approach. Results are presented from two case studies using an openly available Gulf of Mexico AIS dataset as well as a Saint Lawrence Seaway and Great Lakes AIS licensed dataset acquired from ORBCOMM (a maritime AIS data provider). These research findings demonstrate the applicability and scalability of the proposed method for modeling more water regions, contributing to situational awareness, vessel collision prevention, safe navigation, route planning, and detection of vessel behaviour anomalies for auto-vessel development towards the sustainability of marine transportation.

Suggested Citation

  • Xuyang Han & Costas Armenakis & Mojgan Jadidi, 2021. "Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8162-:d:598681
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    References listed on IDEAS

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    1. Pan Sheng & Jingbo Yin, 2018. "Extracting Shipping Route Patterns by Trajectory Clustering Model Based on Automatic Identification System Data," Sustainability, MDPI, vol. 10(7), pages 1-13, July.
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    Cited by:

    1. Eisuke Watanabe & Ryuichi Shibasaki, 2023. "Extraction of Bunkering Services from Automatic Identification System Data and Their International Comparisons," Sustainability, MDPI, vol. 15(24), pages 1-19, December.
    2. 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).
    3. Jorge Ramos & Benjamin Drakeford & Ana Madiedo & Joana Costa & Francisco Leitão, 2024. "A Bayesian Approach to Infer the Sustainable Use of Artificial Reefs in Fisheries and Recreation," Sustainability, MDPI, vol. 16(2), pages 1-16, January.
    4. Jun Zhao & Wenyu Rong & Di Liu, 2023. "Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China," Sustainability, MDPI, vol. 15(8), pages 1-22, April.

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