A Combined Deep Learning Method for Prediction of Ship Traffic Flow

With the rapid development of China's maritime industry, accurate prediction of ship traffic flow is of great significance to improve the operational efficiency of ports. In this paper, we propose a combined model that combines the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Long Short-Term Memory (LSTM) fusing Multi-head Attention mechanism to improve the prediction accuracy of ship traffic flow. The example data summarizes the actual data from Guangzhou port production business system from 2014-2023. The study integrates the predictive capabilities of linear and nonlinear models while considering the influence of external economic factors on ship traffic flow, possessing better forecasting outcomes. Through comparison experiments with other three models, the combined deep learning model shows superior forecasting performance.