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Vessel trajectory prediction based on AIS data and bidirectional GRU

机译：基于AIS数据和双向GRU的血管轨迹预测

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摘要

The prediction of vessel berthing trajectory can provide reference for the supervision of vessel traffic services, and has high application value in the early warning of vessel collision, grounding and other accidents. Aiming at the problem that it is difficult to predict the movement trend of vessels in the crowded port water, this paper establishes a vessel berthing trajectory prediction model based on bidirectional Gated Recurrent Unit (Bi-GRU). By learning the AIS data of Tianjin port, the vessel trajectories are predicted and compared with other recurrent neural network models such as LSTM and GRU. The experimental results show that the prediction method based on Bi-GRU model has higher accuracy and smaller error.
机译：船舶停泊轨迹的预测可以为船舶交通服务的监督提供参考，并且在船舶碰撞，接地和其他事故的预警中具有高的应用价值。旨在难以预测拥挤的港口水中血管运动趋势的问题，本文建立了基于双向门控复发单元（Bi-Gr）的血管拦截轨迹预测模型。通过学习天津端口的AIS数据，预测船舶轨迹并与其他经常性神经网络模型相比，如LSTM和GRU。实验结果表明，基于Bi-Gru模型的预测方法具有更高的精度和更小的误差。

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