Ship Trajectory Prediction Based on CNN-F-LSTM-Attention
With the acceleration of economic globalization and the continuous expansion of international trade,the maritime transportation in-dustry is developing rapidly.In ports with high traffic density and complex conditions,traffic safety management is facing enormous challeng-es.Ship collision is one of the frequent types of accidents at sea,and accurate ship prediction is extremely important for maritime traffic man-agement and ensuring the safety of ship navigation.The commonly used method for predicting ship trajectories is the Long Short Term Memory Network,but it has a large number of gate control weight parameters,a complex structure,and insufficient exploration of spatial and temporal features.A ship trajectory prediction model combining convolutional neural network,improved long short-term memory network,and attention mechanism is proposed to address the above issues.This model reduces structural complexity,improves training speed and generalization per-formance through an improved long short-term memory network;At the same time,convolutional neural networks are introduced to fully ex-plore the spatial and temporal features of trajectory data,and different weights are assigned to different features through attention mechanisms to filter out useless feature information and improve model accuracy.The experimental results on real datasets show that the proposed model has improved accuracy in predicting latitude,longitude,heading,and speed compared to mainstream control models.
track predictiondata preprocessingdeep learningnatural language processingAIS data
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