Multi-Step Trajectory Prediction of Port Container Trucks Based on CT-HybridNet Model

In port environments, container stacking at significant heights obstructs satellite signal reception by terminal equipment on container trucks, leading to inaccurate positional tracking data. To address this, it is necessary to predict container truck trajectories to fill in the inaccurate positioning signals. In this study, we collected port container truck trajectory data and compared the predictive performance of long short-term memory (LSTM) networks and transformer models, revealing performance turning points at different prediction steps. Based on these findings, we propose a hybrid model named CT-HybridNet, which integrates the LSTM-based DeepPBM-M model with the transformer-based PatchTST model. Given the independence of the prediction errors of the two models, we assume both errors follow a Gaussian distribution. By performing an affine transformation, the proposed hybrid method’s output also follows a Gaussian distribution. Additionally, an adaptive parameter adjustment mechanism optimises performance, enabling CT-HybridNet to achieve dual improvements in trajectory prediction accuracy and stability, with 15% improvement in short-term accuracy and 20% in long-term performance. This study provides a more accurate and stable technical solution for port container truck trajectory prediction, overcoming issues related to positioning inaccuracies and signal obstructions.