Resumen
The rapid growth of ship traffic leads to traffic congestion, which causes maritime accidents. Accurate ship trajectory prediction can improve the efficiency of navigation and maritime traffic safety. Previous studies have focused on developing a ship trajectory prediction model using a deep learning approach, such as a long short-term memory (LSTM) network. However, a convolutional neural network (CNN) has rarely been applied to extract the potential correlation among different variables (e.g., longitude, latitude, speed, course over ground, etc.). Therefore, this study proposes a deep-learning-based ship trajectory prediction model (namely, CNN-LSTM-SE) that considers the potential correlation of variables and temporal characteristics. This model integrates a CNN module, an LSTM module and a squeeze-and-excitation (SE) module. The CNN module is utilized to extract data on the relationship among different variables (e.g., longitude, latitude, speed and course over ground), the LSTM module is applied to capture temporal dependencies, and the SE module is introduced to adaptively adjust the importance of channel features and focus on the more significant ones. Comparison experiments of two cargo ships at a time interval of 10 s show that the proposed CNN-LSTM-SE model can obtain the best prediction performance compared with other models on evaluation indexes of average root mean squared error (ARMSE), average mean absolute percentage error (AMAPE), average Euclidean distance (AED), average ground distance (AGD) and Fréchet distance (FD).