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Data-Driven Prediction of Ship Destinations in the Harbor Area Using Deep Learning

机译：利用深度学习的港口地区船舶目的地的数据驱动预测

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The prediction of ship destinations in the harbor can be utilized to identify future routes for navigating ships. The maritime traffic data are broadly classified into the ship trajectory data and the port information management data. These data have been accumulated for many years on the shore base station of different agencies, and are being utilized for evaluation of collision risk, prediction of vessel traffic, and other maritime statistical analysis. This paper presents a new destination prediction model of navigating ships in the harbor which consists of the candidate harbor proposal module and the position-direction filter module. The candidate harbor proposal module is trained by a deep neural network which makes use of the characteristics of ships and the occupancy distributions of piers. The position-direction filter module leaves out non-promising ones from the harbor list provided by the candidate proposal module, with respect to the current position and direction of navigating ship. In the experiments on real vessel traffic data, the proposed method has shown that its accuracy is higher than the frequency-based baseline method by about 10-15%.
机译：港口中船舶目的地的预测可用于确定导航船舶的未来路线。海上流量数据广泛分类为船舶轨迹数据和端口信息管理数据。这些数据已经积累了多年的不同机构的岸上基站，并且正在用于评估碰撞风险，船舶交通预测和其他海上统计分析。本文介绍了港口航线船舶的新目的地预测模型，包括候选港口提案模块和位置方向滤波器模块。候选港口提案模块受到深度神经网络的培训，这是利用船舶的特点和码头的占用分布。位置方向过滤器模块从候选提案模块提供的港口列表中留下了非承诺的，关于导航船的当前位置和方向。在真实船舶交通数据的实验中，所提出的方法表明，其精度高于基于频率的基线方法约10-15％。

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