针对船舶交通流量大、水域繁忙情况下，通行能力预测难度过高的问题，提出船舶拥堵水域通行能力预测技术。利用船舶坐标位置以及航行速度，确定船舶状态，建立船舶航行队列模型。依据船舶领域模型确定船舶航行对应的椭圆形区域，作为船舶拥堵水域的领域。确定船舶领域内，影响航道容量的相关因素。设置船舶密度、船舶航行速度等水域通行能力影响指标，作为长短时记忆网络的输入。长短时记忆网络通过遗忘门、输入门以及输出门3个门控单元，选择保留或遗忘输入的数据，输出船舶拥堵水域通行能力预测结果。实验结果表明，该技术能够有效预测船舶拥堵水域通行能力，提高航道利用率，保障水域交通安全性。
Aiming at the problem of high difficulty in predicting traffic capacity in congested water areas due to high ship traffic flow and busy waters, a technology for predicting traffic capacity in congested water areas is proposed. Determine the ship's status and establish a ship navigation queue model by utilizing the ship's coordinate position and navigation speed. Based on the Fujino ship domain model, determine the elliptical area corresponding to ship navigation as the domain of ship congestion waters. Determine the relevant factors that affect the capacity of shipping channels within the field of ships. Set indicators such as ship density, ship speed, and other factors that affect water traffic capacity as inputs for the long short-term memory network. The long short-term memory network uses three gating units, namely the forget gate, input gate, and output gate, to select whether to retain or forget the input data, and output the prediction results of ship traffic capacity in congested water areas. The experimental results show that this technology can effectively predict the traffic capacity of ship congested waters, improve the utilization rate of waterways, and ensure the safety of water traffic.
2024,46(12): 170-173 收稿日期：2024-01-16
DOI：10.3404/j.issn.1672-7649.2024.12.030
分类号：U612
作者简介：李道科(1979-),男,硕士,副教授,研究方向为船舶通航安全及航海技术
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