• Title/Summary/Keyword: Vessl Traffic System Operator

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Prediction of Ship Travel Time in Harbour using 1D-Convolutional Neural Network (1D-CNN을 이용한 항만내 선박 이동시간 예측)

  • Sang-Lok Yoo;Kwang-Il Ki;Cho-Young Jung
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2022.06a
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    • pp.275-276
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    • 2022
  • VTS operators instruct ships to wait for entry and departure to sail in one-way to prevent ship collision accidents in ports with narrow routes. Currently, the instructions are not based on scientific and statistical data. As a result, there is a significant deviation depending on the individual capability of the VTS operators. Accordingly, this study built a 1d-convolutional neural network model by collecting ship and weather data to predict the exact travel time for ship entry/departure waiting for instructions in the port. It was confirmed that the proposed model was improved by more than 4.5% compared to other ensemble machine learning models. Through this study, it is possible to predict the time required to enter and depart a vessel in various situations, so it is expected that the VTS operators will help provide accurate information to the vessel and determine the waiting order.

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