# A Ship Trajectory Prediction Method Based on an Optuna-BILSTM Model
## 4 Conclusions  
In this study, we have proposed a model that combines Optuna and BILSTM for predicting ship trajectories. The model is designed to overcome the limitations of traditional BP neural networks in dealing with long-time-series problems and to perform hyperparameter optimization using Optuna. The aim is to improve the accuracy and performance of trajectory prediction. In this study, trajectory data from four vessels were used as a benchmark to evaluate the model's performance. By comparing with traditional BP neural network, LSTM and BILSTM models, this study validates the superiority of the proposed Optuna-BILSTM model. The results show that the model exhibits higher accuracy and robustness in the ship trajectory prediction task. The hyperparameter optimization of Optuna enables an automatic search for the best model hyperparameter configuration, which further improves the model performance. This combined approach allows for a better adaptation of the model structure and parameter settings to specific ship trajectory prediction tasks. In addition, this study reveals another advantage of the proposed model, namely its ability to extract important features. By learning patterns and trends in the trajectory data, the model is able to capture the key features that influence the ship's trajectory. This ability can be used to reduce the error in predicting the ship's tracking point by using the extracted features. Consistent with the aims of scholar [PERSON]'s research, which emphasizes the enhancement of maritime safety and the prevention of ship collisions at sea, the Optuna-BILSTM trajectory prediction model holds significant promise [34]. With its ability to effectively predict vessel trajectories based on historical data, the model can play a pivotal role in mitigating the risk of collisions and ensuring safer maritime navigation.  
Ultimately, this research has practical applications for maritime traffic controllers. By accurately predicting ship trajectories, ships can take the necessary precautions in advance to avoid collisions. This not only helps to improve the efficiency of maritime traffic but also enhances maritime safety.  
Conceptualization, X.B. and Y.Z.; Methodology, Y.Z.; Software, Y.Z.; Validation, Y.Z., and Z.D.; Formal Analysis, Y.Z.; Investigation, Y.Z.; Resources, X.B.; Data Curation, Z.D.; Writing--Original Draft Preparation, Y.Z.; Writing--Review and Editing, X.B.; Visualization, Y.Z.; Supervision, X.B.; Project Administration, X.B.; Funding Acquisition, X.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ningbo International Science and Technology Cooperation Project: Theoretical and Technological Research on Cooperative Traffic Control of Port-Collector-Supply Highway, grant number 2023H020, and the National Natural Science Foundation of China (NNSF) Top Project: Traffic State Estimation and Control Methods for Port-Collector-Supply Roads in Project Networked Vehicle Environment, grant number 52272334.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.