A Ship Trajectory Prediction Model Based on Attention-BILSTM Optimized by the Whale Optimization Algorithm

Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, collision avoidance and route planning can be achieved by predicting the ship’s trajectory, which can give crews warning to avoid dangers. How to predict the ship’s trajectory more accurately is of great significance for risk avoidance. However, existing ship trajectory prediction models suffer from problems such as poor prediction accuracy, poor applicability, and difficult hyperparameter design. To address these issues, this paper adopts the Bidirectional Long Short-Term Memory (BILSTM) model as the base model, as it considers contextual information of time-series data more comprehensively. Meanwhile, to improve the accuracy and fitness of complex ship trajectories, this paper adds an attention mechanism to the BILSTM model to improve the weight of key information. In addition, to solve the problem of difficult hyperparameter design, this paper optimizes the hyperparameters of the Attention-BILSTM network by fusing the Whale Optimization Algorithm (WOA). In this paper, the AIS data are filtered, and the trajectory is complemented by the cubic spline interpolation method. Using the pre-processed AIS data, this WOA-Attention-BILSTM model is compared and assessed with traditional models. The results show that compared with other models, the WOA-Attention-BILSTM prediction model has high prediction accuracy, high applicability, and high stability, which provides an effective and feasible method for ship collision avoidance, maritime surveillance, and intelligent shipping.