Stacked LSTMs short-term ship traffic flow prediction model based on attention mechanism
A stacked LSTM ship traffic flow prediction model based on attention mechanism was proposed to address the problem of low prediction accuracy caused by the nonlinear and non-stationary characteristics of short-term ship traffic flow da-ta.Stacked LSTM neural network was used to capture temporal features of short-term ship traffic flow data,and the attention mechanism was introduced to better learn global features,and improved accuracy of ship traffic flow prediction.Ship AIS data were extracted from three sections of the lower reaches of the Yangtze river to construct the ship traffic flow dataset,and used for training and testing of the proposed model.Results show that compared to baseline models such as HA,ARIMA,GPR,LSTM,and Seq2Seq,the model proposed in this paper,both the root mean square error and mean absolute error reduce in predicting macro traffic flow parameters.Compared with the optimal baseline model,the proposed model exhibits higher ac-curacy in predicting ship traffic flow,with a root mean square error reduction of 4.05％and a mean absolute error reduction of 4.04％.
万方数据
维普
