## 2 Related Work  
The prediction of maritime traffic conditions is one of the most challenging tasks, especially when talking about Maritime Autonomous Surface Ships. The automation challenges may vary depending on the scenario between land and sea. Vehicles on the road or highways usually travel in a lane; if we consider ships, they can travel in any direction depending on the sea currents' directions. Furthermore, compared to the vehicle traffic regulations, the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is dependent upon the operator experience, which is less accurately modified. Consequently, exertion will be increased to predict a ship's behavior. The studies about prediction of maritime traffic conditions are based on trajectory prediction and index prediction. The linear model of trajectory prediction can deal with the prediction of trajectories when the ship sails straight; these linear models (LM) are called constant velocity models (CVM). For Example, [PERSON] and [PERSON] [1] proposed an ocean vessel trajectory prediction based on an Extended Kalman filter with a curvilinear motion model with the measurements by a linear position model. [PERSON] et al. [2] proposed an Ornstein-Uhlenbeck process based on control theory, and the method was based on the OU stochastic process used for ship prediction. Historical patterns of life estimated the parameters. [PERSON] et al. [3] proposed a novel technique for long-term target state prediction, and the method is based on Stochastic Mean-Reverting Process. [PERSON] et al. [4] developed a three-step closest point of approach (CPA) search method to accurately predict the ship's future trajectory. The AIS data used in this paper were based on the Speed OverGround (SOG), Course Over Ground (COG), Change of Speed (COS), and rate of turn (ROT) data. However, the prediction model's accuracy depends on the label and physical knowledge recognition. [PERSON] [5] proposed a discrete Kalman filtering algorithm for dynamic data estimation to estimate the missing ship's position distribution of trajectory points linearly. [PERSON] et al. [6] solved a trajectory prediction problem with wavelet analysis; the state transition model they used is based on the Hidden Markov Model to accurately predict the ship's future position and large ship trajectory by constructing a Markov chain. Currently, for predictive traffic management and monitoring, Kongsberg's C-SCOPE solution [7] is the current commercial production system mainly dependent on linear models. Nevertheless, the LM-based techniques perform uncertainly when the vessel wants to change speed or course and tend to drift under specific conditions. Consequently, to overcome these limitations of linear models, many papers presented nonlinear models to enhance prediction accuracy. The conventional nonlinear marine dynamics [8] utilized machine learning and deep learning [9]. However, each ML and DL strategy has different benefits and applications. Sometimes ML makes good predictions, while deep learning is time and space-consuming. On the other hand, deep learning is outstanding for large amounts of data, and outperforms ML models. [PERSON] et al. [10] proposed an enhanced neurobiological-inspired algorithm, which takes real-time maritime tracking data to learn motion pattern models for situation awareness. [PERSON] et al. [11] proposed a combination of grey prediction and an improved Markov model for island vessel trajectory prediction based on AIS data. [PERSON] and [PERSON] [12] presented a machine learning and data mining-based vessel trajectory prediction. The main algorithm they used is a support vector machine (SVM) to cluster trajectories and classify them. [PERSON] et al. [13] proposed a big data analytics method to evaluate ship grounding risk in actual environmental circumstances, and they also applied big data on AIS nowcast data. [PERSON] et al. [14] presented an ensemble machine-learning framework for basic AIS data cleaning and removed all outliers in the data.