Stacked LSTMs Short-Term Ship Traffic Flow Prediction Model based on Attention Mechanism

LIAN Qingyun , SUN Wei, LI Runsheng

doi:10.16411/j.cnki.issn1006-7736.2024.01.007

Journal of Dalian Maritime University >

2024 , Vol. 50 >Issue 1: 57 - 65

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2024.01.007

Stacked LSTMs Short-Term Ship Traffic Flow Prediction Model based on Attention Mechanism

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（1.College of Merchant Marine, Shanghai Maritime University, Shanghai 201306；2. College of Information Engineering, Shanghai Maritime University, Shanghai 201306）

Received date: 2023-07-15

Revised date: 2023-10-09

Accepted date: 2023-10-09

Online published: 2023-10-09

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Abstract

According to address the issue of poor prediction accuracy caused by the nonlinear and non-stationary characteristics of short-term ship traffic flow data, this paper proposes an attention-based stacked LSTM ship traffic flow prediction model. The model primarily utilizes stacked LSTM neural networks to capture the temporal features of short-term ship traffic flow data, and introduces the attention mechanism to enhance the learning of global features and improve the accuracy of ship traffic flow prediction. The ship Automatic Identification System (AIS) data from three segments in the lower reaches of the Yangtze River are extracted and employed to construct the ship traffic flow datasets for training and testing the model. The results demonstrate that compared to baseline models such as HA, ARIMA, GPR, LSTM, and Seq2Seq, the model proposed in this paper reduces both the root mean square error and mean absolute error for predicting macro traffic flow parameters. This model demonstrates improved accuracy in ship traffic flow prediction compared to the optimal baseline model, achieving a reduction of 4.05% in root mean square error and 4.04% in mean absolute error.

Key words： river transportation; ship traffic flow prediction; LSTM; stacked LSTM; attention mechanism

Cite this article

LIAN Qingyun , SUN Wei, LI Runsheng . Stacked LSTMs Short-Term Ship Traffic Flow Prediction Model based on Attention Mechanism[J]. Journal of Dalian Maritime University, 2024 , 50(1) : 57 -65 . DOI: 10.16411/j.cnki.issn1006-7736.2024.01.007

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