Adaptive multi-source data fusion vessel trajectory prediction model for intelligent maritime traffic

Multi-source vessel automatic identification system (AIS) data gathered in the maritime Internet of Things (IoT) system has diverse data characteristics, such as sparse satellite data and terrestrial receiver's limited coverage. However, it is challenging for the maritime IoT system to provide efficient and accurate intelligent trajectory prediction services while applying multi-source AIS data. Therefore, improving the adaptability and accuracy of vessel trajectory prediction methodologies adapted to varied AIS datasets is essential to enhancing marine traffic safety and transportation efficiency. This study proposes an adaptive data fusion (ADF) model based on multi-source AIS data for predicting vessel trajectories in maritime traffic. The same marine mobile service identifier (MMSI) and timestamp fuse the multi-source AIS data. Deep learning methods are employed to perform feature learning and increase adaptability. This study also utilizes adjacency distance to enhance the correlation between dataset trajectory trajectories. Finally, we validate the benefits of the ADF model using actual terrestrial and satellite AIS data along the west coast of the United States (US). Regarding average RMSE and MAE, the ADF model reduces the prediction error by 46.51% and 52.7% when fusing multi-source historical trajectories compared to current studies. Moreover, multiple ablation experiments have proved our model's superiority.