## 1 Introduction  
The application of information and communication technology, big data, and artificial intelligence technology to the conventional concept of ships has led to the emergence of futuristic ships, which are safer and more efficient compared to conventional ships. In general, futuristic ship refers to a Maritime Autonomous Surface Ship that can navigate without intervention from the crew. Thus far, several countries have implemented policy support and government-led projects to preempt the autonomous ship industry. In South Korea, the \"autonomous ship technology development project\" is being jointly conducted by the Ministry of Oceans and Fisheries and the Ministry of Trade, Industry, and Energy.  
In the commercialization of autonomous ships, it is necessary to develop appropriate technologies including Intelligent navigation systems, Engine automation systems, Performance verification center and verification technology, and Operation technology and standardization, which represent the core components of the 'Intelligent navigation system' that will replace navigators and captains.  
The 'Intelligent navigation system' consists of situation recognition of maritime traffic conditions, generation and following of routes, collision avoidance, and returning routes.  
The situation recognition of maritime traffic conditions involves the identification of the current traffic conditions and the prediction of future traffic conditions. This is a prior technology that is essential to ensure the safe navigation of autonomous ships. Situation recognition technology for determining maritime traffic conditions generally involves the use of cameras or the identification of the current maritime traffic conditions based on traffic data. This aspect is a critical component in the development of route generation algorithms or the collision avoidance algorithms of autonomous ships.  
In the process of recognizing a situation, navigators predict future maritime traffic conditions using empirical and intuitive judgment based on traffic data in addition to a visual assessment of traffic conditions. Based on these factors, decisions are made regarding the safety of a route and collision avoidance. Therefore, for an autonomous ship to generate a safer route and to efficiently perform collision avoidance considering future maritime traffic conditions, it is necessary to predict future maritime traffic conditions as well as to assess current conditions.  
Consequently, in this study, we aimed to develop an algorithm that predicts future traffic conditions based on past traffic data to improve the performance of the recognition system of autonomous ships. It was determined that the developed algorithm can be used not only to improve the recognition system of autonomous ships but can also be applied to maritime traffic condition recognition for coastal ship navigation along complex routes.  
The Main Contributions of the paper are as follows:  
* Development of an intelligent LSTM-based prediction algorithm for Maritime Traffic Conditions;
* Feature Augmentation for maritime traffic congestion;
* Utilizing large amount of data for traffic conditions;
* Motivation to develop Maritime Autonomous Surface Ships;
* Development of an Intelligent navigation system.  
The rest of the paper is structured as follows: We briefly discuss the related work in Section 2, where we discussed different kind of maritime traffic condition predictions with linear and nonlinear methodologies. In Section 3, we discussed the theory of maritime traffic conditions. In Section 4, the LSTM and feature augmentation-based prediction algorithm of maritime traffic condition is discussed in detail with experimental results. In Section 5, the algorithm's validation is given. Finally, the conclusions are drawn in Section 6.