## 6 Conclusions  
This study proposed a method of using data augmentation and LSTM to predict future maritime traffic conditions. Scenarios were established by combining various features, and model training and evaluation were performed. The results confirmed that the performance of the model improved as more features were applied. The maximum values for 3, 6, 12, and 24 days and the congestion of the gate lines around the analysis point showed a significant effect on performance. The algorithm validation results based on the length of train/validate data and test data indicate that a higher performance was observed with an increase in the size of data. The performance was guaranteed to some extent when data for more than a week were predicted by constructing the model using the data of at least three months.  
The limitations of this study are as follows:  
1. Maritime traffic congestion was predicted by applying the developed algorithm to a specific point in one port; therefore, it is necessary to perform evaluation for various points in several ports.
2. The congestion of nearby gate lines was used as a feature. This could decrease the accuracy of the algorithm if the influence of the nearby gate lines on the gate line to be predicted is insignificant based on the scenario.  
Despite these limitations, the results of this study can be used to improve the performance of the situation recognition system of autonomous ships for identifying the current maritime traffic conditions and predicting the future maritime traffic conditions by identifying features for predicting maritime traffic conditions, constructing a model, and presenting the appropriate length of data for prediction. Further, they are expected to be used in maritime traffic condition recognition technology for coastal ships that navigate more complex sea routes compared to ships navigating the ocean. As a future study, we plan to evaluate algorithms developed for various ports and prepare countermeasures for cases where the influence of neighboring gate lines is small as a feature for predicting maritime traffic congestion. In addition, the performance of this algorithm will be validated by combining the situational awareness system of an autonomous ships with generation routes, collision avoidance, and returning routes of a coastal autonomous ships navigating complex sea areas.  
Figure 9: Comparison of results with other prediction model [17].  
**Author Contributions:** Conceptualization, K.K. and E.L.; methodology, E.L.; software, E.L.; validation, E.L., J.K. and W.-J.S.; data curation, E.L.; writing--original draft preparation, E.L.; writing--review and editing, J.K., W.-J.S. and K.K.; visualization, E.L.; supervision, K.K.; project administration, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was partly supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University)).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data can be obtained by contacting [EMAIL_ADDRESS].  
**Conflicts of Interest:** The authors declare no conflict of interest.