A Short-Term Vessel Traffic Flow Prediction Based on a DBO-LSTM Model

Abstract

To facilitate the efficient prediction and intelligent analysis of ship traffic information, a short-term ship traffic flow prediction method based on the dung beetle optimizer (DBO)-optimized long short-term memory networks (LSTM) is proposed. Firstly, according to the characteristics of vessel traffic flow, speed, and density, the traffic flow parameters are extracted from the AIS data; secondly, the DBO-LSTM model is established, and the optimal hyperparameter combinations of the LSTM are found using the DBO algorithm to improve the model prediction accuracy; then, taking the AIS data of a part of the coastal port area in Xiangshan as an example, we compare and analyze the results of the recurrent neural network, temporal convolutional network, LSTM, and DBO-LSTM prediction models; finally, the results are displayed and analyzed by visualization. The experimental results show that each error is reduced in predicting the flow parameter, speed parameter, and density parameter, and the accuracy reaches 95%, 92%, and 95%, respectively. After predicting the three parameters in the next 24 h, the accuracy rate reaches 93%, 91%, and 94%, respectively, compared with the real data, which surpasses the comparison model and achieves better prediction accuracy, verifying the feasibility and reasonableness of the proposed prediction model.