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Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining

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  • Hailin Zheng

    (College of Merchant Marine, Shanghai Maritime University, Shanghai 201306, China
    College of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China)

  • Qinyou Hu

    (College of Merchant Marine, Shanghai Maritime University, Shanghai 201306, China)

  • Chun Yang

    (College of Merchant Marine, Shanghai Maritime University, Shanghai 201306, China)

  • Jinhai Chen

    (College of Navigation, Jimei University, Xiamen 361021, China
    National-Local Joint Engineering Research Center for Marine Navigation Aids Services, Xiamen 361021, China)

  • Qiang Mei

    (College of Navigation, Jimei University, Xiamen 361021, China
    National-Local Joint Engineering Research Center for Marine Navigation Aids Services, Xiamen 361021, China)

Abstract

Since the spread of the coronavirus disease 2019 (COVID-19) pandemic, the transportation of cargo by ship has been seriously impacted. In order to prevent and control maritime COVID-19 transmission, it is of great significance to track and predict ship sailing behavior. As the nodes of cargo ship transportation networks, ports of call can reflect the sailing behavior of the cargo ship. Accurate hierarchical division of ports of call can help to clarify the navigation law of ships with different ship types and scales. For typical cargo ships, ships with deadweight over 10,000 tonnages account for 95.77% of total deadweight, and 592,244 berthing ships’ records were mined from automatic identification system (AIS) from January to October 2020. Considering ship type and ship scale, port hierarchy classification models are constructed to divide these ports into three kinds of specialized ports, including bulk, container, and tanker ports. For all types of specialized ports (considering ship scale), port call probability for corresponding ship type is higher than other ships, positively correlated with the ship deadweight if port scale is bigger than ship scale, and negatively correlated with the ship deadweight if port scale is smaller than ship scale. Moreover, port call probability for its corresponding ship type is positively correlated with ship deadweight, while port call probability for other ship types is negatively correlated with ship deadweight. Results indicate that a specialized port hierarchical clustering algorithm can divide the hierarchical structure of typical cargo ship calling ports, and is an effective method to track the maritime transmission path of the COVID-19 pandemic.

Suggested Citation

  • Hailin Zheng & Qinyou Hu & Chun Yang & Jinhai Chen & Qiang Mei, 2021. "Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1089-:d:484377
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    References listed on IDEAS

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    1. Xinqiang Chen & Jinquan Lu & Jiansen Zhao & Zhijian Qu & Yongsheng Yang & Jiangfeng Xian, 2020. "Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    2. Ducruet, César, 2017. "Multilayer dynamics of complex spatial networks: The case of global maritime flows (1977–2008)," Journal of Transport Geography, Elsevier, vol. 60(C), pages 47-58.
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    Cited by:

    1. Dirzka, Christopher & Acciaro, Michele, 2022. "Global shipping network dynamics during the COVID-19 pandemic's initial phases," Journal of Transport Geography, Elsevier, vol. 99(C).
    2. Arthur J. Lin & Hai-Yen Chang & Brian Hung, 2022. "Identifying Key Financial, Environmental, Social, Governance (ESG), Bond, and COVID-19 Factors Affecting Global Shipping Companies—A Hybrid Multiple-Criteria Decision-Making Method," Sustainability, MDPI, vol. 14(9), pages 1-29, April.

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