基于AIS数据和LSTM网络的船舶航行动态预测

Ship navigation behavior prediction based on AIS data and LSTM network

为进一步提高船舶航行动态预测的精度,提出将长短期记忆(long short-term memory,LSTM)网络应用到船舶航行动态预测。将船舶自动识别系统(automatic identification system,AIS)数据转化为航行动态时间序列数据用于LSTM网络的训练和测试。将预测结果与传统的航迹推算法、BP(back propagation)神经网络法等的预测结果进行对比,结果证明:基于LSTM网络的船舶航行动态预测模型具有精度高、鲁棒性强、通用性好等特点。预测结果可以为船舶交通管理中心(vessel traffic services,VTS)的监管提供参考,在预警船舶碰撞、搁浅等事故方面具有较高的实际应用价值。

In order to further improve the accuracy of ship navigation behavior prediction,the long short-term memory(LSTM)network is applied to the ship navigation behavior prediction.The ship automatic identification system(AIS)data is transformed into navigation behavior time series data for training and testing of LSTM network.The prediction results of LSTM network are compared with those of the traditional track estimation method and BP(back propagation)neural network method.The results show that the ship navigation behavior prediction model based on LSTM network is of high accuracy,strong robustness and good generality.The prediction results can provide reference for the supervision of vessel traffic services(VTS),and have high practical application value in early warning of ship collision,stranding and other accidents.