Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping

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Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping

The article discusses the importance of predicting Vessel Traffic Flow (VTF) in the maritime industry using big data from the Automatic Identification System (AIS). It introduces a new learning-based prediction network that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with similarity grouping to enhance accuracy.

The proposed methodology transforms one-dimensional data into a matrix to capture spatial and temporal characteristics of VTF. By exploring hidden relationships between adjacent regions, the model aims to improve prediction results by considering the influence of VTF changes in different areas. Comparative experiments show that the new method outperforms eleven other existing methods in terms of accuracy and stability.

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