## 2 Related Work
The clean data are used to predict trajectory variation tendency in ships. The method is verified with three ship trajectory segments. [PERSON] et al. [15] did a comprehensive survey from data to methodology and argued that new algorithms must be developed for accurate maritime traffic conditions. Most of the present path prediction algorithms are built on a fast update prediction method such as the Kalman filter. Due to the popularity and advancement of deep learning methods [16], efficient data-driven methods are becoming increasingly popular among researchers. The index prediction includes predicting quantitative indicators expressing various maritime traffic conditions, including congestion, density, risk of collision, predicting maritime accidents, and so on. [PERSON] et al. [17] proposed a method that predicted maritime traffic congestion by combining the automatic identification systems of ships and port management information data. [PERSON] et al. [18] used timeseries models and the associative models to predict the maritime traffic density of Port Klang and the Straits of Malacca. For the prediction of maritime traffic flow, [PERSON] et al. [19] used deep learning solutions such as CNN, LSTM, and BDLSTM-CNN. [PERSON] et al. [20] used an improved PSO-BP mechanism which is a self-adaptive particle swarm optimization-back propagation. Lastly, [PERSON] et al. [21] used a Spatio-Temporal Multigraph Convolutional Network to achieve fine-grained vessel traffic flow prediction. In order to prevent marine accidents, the possibility of maritime accidents can be predicted, and there is also a method of predicting the risk of collision, which is the main cause of marine accidents. [PERSON] et al. [22] and [PERSON] and [PERSON] [23] proposed the system to predict regional collision risk using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique and a Recurrent Neural Network (RNN). To predict maritime accidents, [PERSON] et al. [24] used Machine learning (ML) technology such as the random forest (RF) model, Adaboost model, gradient boosting decision tree (GBDT) model, and Stacking combined model and compared the result. [PERSON] and [PERSON] [25] proposed a stochastic model using the geographical characteristics of the Istanbul strait and visualized risk map. There are studies that have used modern AI algorithms to make predictions; [PERSON] et al. [26] used Artificial Intelligence to improve energy efficiency in buildings. The focus of the article is on the implementation of an AI model that predicts room occupancy based on thermal comfort parameters such as temperature, humidity, and air velocity. [PERSON] et al. [27] focused on the application of artificial intelligence techniques for predicting and detecting marine corrosion. They present case studies and experimental results to demonstrate the efficacy of these techniques in detecting and predicting marine corrosion and conclude that the application of artificial intelligence has the potential to revolutionize the field of marine corrosion prediction and detection. [PERSON] et al. [28] propose an algorithm that predicts future ship speed based on past data using a deep learning model. They also perform linear interpolation to handle missing data and apply feature augmentation to improve the performance of the model. The results of the study show that the proposed algorithm outperforms existing methods in terms of prediction accuracy. In the above related studies, there was no study to identify which feature affected the prediction of maritime traffic congestion. Therefore, in this study, we tried to find the significant features for predicting maritime traffic congestion through feature augmentation and build a prediction algorithm of maritime traffic congestion using LSTM.