Journal of Geo-information Science ›› 2022, Vol. 24 ›› Issue (9): 1688-1700.doi: 10.12082/dqxxkx.2022.210710

Previous Articles     Next Articles

Research on the Refined Segmental Tide Riding of Large Vessels based on Multi-Source Maritime Data

ZHANG Xinyu1,*(), GUO Wenqiang1, WANG Jingyun1, YANG Bingdong2   

  1. 1. DaLian Maritime University, Maritime Intelligent Transportation Research Team, Dalian 116026, China
    2. Huanghua Port Pilot Station, Cangzhou 061000, China
  • Received:2021-11-05 Revised:2022-02-11 Online:2022-09-25 Published:2022-11-25
  • Contact: ZHANG Xinyu E-mail:zhangxy@dlmu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(51779028)

Abstract:

Aiming at the problem of insufficient time for large vessels to enter the port by tide in the long channel, this paper proposed a refined segmented tide riding model for large vessels based on multi-source maritime data such as AIS data, port tide data, electronic chart data, and so on. Firstly, based on the AIS data, the K-medoids algorithm was used to mine the characteristics of the vessel's tide riding behavior, identify the key points of the vessel's tide riding trajectory, and calculate the key vessel position points of the vessel's tide-riding behavior change. Then, combing the geographical environment characteristics of the long channel and the characteristics of the navigation behavior of the vessels, the long channel was refined and segmented. Based on the port tide data, a refined calculation model of the vessel's segmented tide riding window period was constructed. Secondly, we designed an adaptive arrangement algorithm for the tide riding duration to solve the longest window period of the vessel's tide riding. Taking the Huanghua Port integrated port area as an example, the refined segmented tide riding model proposed in this paper was verified. Finally, based on the electronic chart data, the geographic information system platform was used to realize the fine segmented three-dimensional (3D) dynamic deduction of the vessel's tide riding, to further verify the safety of the vessel's tide navigation. The results show that the model can effectively increase the window period for vessels to enter the port by tide and improve the efficiency of ships entering the port by tide. This study can provide theoretical guidance for the port and shipping management departments to formulate vessel entry plans.

Key words: multi-source maritime data, K-medoids, refinement, ride the tide in stages, window period, duration sort, GIS, 3D dynamic deduction