CNN-GRU ship traffic flow prediction model based on attention mechanism

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  • (Navigation College, Dalian Maritime University, Dalian 116026,China)

Online published: 2023-01-20

Abstract

In order to predict inland waterway ship traffic flow more accurately, a CNN-GRU ship traffic flow prediction model was proposed based on attention mechanism. The model mainly extracted the high-dimensional features of the data by means of  one-dimensional convolution unit. The GRU unit learned the temporal characteristics in the data and enhanced the learning of important features by introducing attention mechanism to realize the learning of ultra-long sequences. In addition, by analyzing the correlation between the upper and lower waterway traffic flows, the ship AIS data of 6 sections in  the middle and lower reaches of the Yangtze River were extracted to construct the multi-segment ship traffic flow sequence data set and then input into the proposed model for training and testing. The  results show that compared with SAE, LSTM, GRU, CNN+GRU and GRU+Attention in the series prediction model,the proposed model has higher prediction accuracy in the prediction of different traffic flow parameters, and the prediction accuracy of traffic flow, traffic flow density and traffic flow speed are 95.42%, 97.33% and 94.99% respectively, which can better meet the needs of engineering applications.

Cite this article

WU Yingying, ZHAO Lining, YUAN Zhixin, ZHANG Can . CNN-GRU ship traffic flow prediction model based on attention mechanism[J]. Journal of Dalian Maritime University, 2023 , 49(1) : 75 -84 . DOI: 10.16411/j.cnki.issn1006-7736.2023.01.008

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