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Extracting Shipping Route Patterns by Trajectory Clustering Model Based on Automatic Identification System Data

Author

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  • Pan Sheng

    (State Key Laboratory of Ocean Engineering, Department of International Shipping, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Jingbo Yin

    (State Key Laboratory of Ocean Engineering, Department of International Shipping, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Shipping route analysis is essential for vessel traffic management and relies on professional technical facilities for collecting and recording specific information about vessel behaviors. The recent Automatic Identification System (AIS) onboard has been made available to provide ship-related information for the research. However, the complexity and large quantity of AIS data overload traditional surveillance operations and increase the difficulty of vessel traffic analysis. An unsupervised approach is urgently desired to effectively convert the raw AIS data to regular shipping route patterns. In this paper, we proposed a trajectory clustering model based on AIS data to analyze the shipping routes. The whole model consists of four parts: Data preprocessing, structure similarity measurement, clustering, and representative trajectory extraction. Our model comprehensively considered the geospatial information and contextual features of ship trajectory. The revised density-based clustering algorithm could automatically classify different shipping routes with trajectory features without prior knowledge. The experimental evaluation showed the effectiveness of the proposed model by real AIS data from Port of Tianjin. The results contribute to the further understanding of shipping route patterns and assists maritime authorities and the officers in stable and sustainable vessel traffic management.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2327-:d:156357
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    Citations

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    Cited by:

    1. Andreas Komninos & Charalampos Kostopoulos & John Garofalakis, 2022. "Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies," Information Technology & Tourism, Springer, vol. 24(2), pages 265-298, June.
    2. Junhao Jiang & Yi Zuo, 2023. "Prediction of Ship Trajectory in Nearby Port Waters Based on Attention Mechanism Model," Sustainability, MDPI, vol. 15(9), pages 1-31, April.
    3. Carlos Pais-Montes & Jean-Claude Thill & David Guerrero, 2024. "Identification of shipping schedule cancellations with AIS data: an application to the Europe-Far East route before and during the COVID-19 pandemic," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(3), pages 490-508, September.
    4. 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.
    5. Fuentes, Gabriel, 2021. "Generating bunkering statistics from AIS data: A machine learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    6. Pegado-Bardayo, Ana & Lorenzo-Espejo, Antonio & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo, 2023. "A data-driven decision support system for service completion prediction in last mile logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).

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