# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
###### Abstract  
The prediction of vessel maritime navigation has become an exciting topic in the last years, especially considering economics, commercial exchange, and security. In addition, vessel monitoring requires better systems and techniques that help enterprises and governments to protect their interests. Specifically, the prediction of vessel movements is essential for safety and tracking. However, the applications of prediction techniques have a high cost related to computational efficiency and low resource saving. This article presents a sample method to select historical data on vessel-specific routes to optimize the computational performance of the prediction of vessel positions and route estimation in real-time. These historical navigation data can help to estimate a complete path and perform vessel position predictions through time. This Select Best AIS Data in Prediction Vessel Movements and Route Estimation (PreMovEst) method works in a Vessel Traffic Service database to save computational resources when predictions or route estimations are executed. This article discusses AIS data and the artificial neural network. This work aims to present a prediction model that correctly predicts the physical movement in the route. It supports path planning for the Vessel Traffic Service. After testing the method, the results obtained for route estimation have a precision of 76.15%, and those for vessel position predictions through time have an accuracy of 81.043%. select AIS data; route estimation; neural networks +
Footnote †: journal: Medical  
Likewise, to find vessel movements and prevent incidents, techniques such as maritime tracking and vessel monitoring systems have been proposed. For the latter, in 2002, the International Maritime Organization (IMO) approved the Automatic Identification System (AIS), which has now become an indispensable tool in terms of safety and vessel monitoring around the world. The vessel industry is volatile, and so commerce and the carriers collaborate and cooperate in alliances. The industry is characterized by having very different freight rates depending on the direction of the trip. Several carriers should deliver the products in several journeys based on the origin country and destination. In 2020, with the COVID-19 pandemic, the number of vessels carrying goods to satisfy client demand has been growing a lot. The origin and destination of vessels imply costs related to the physical routes; however, the costs should be reduced by selecting the optimal routes. Besides, to ensure driver adherence to routes and other requirements for the vessel, there should always be a degree of maneuverability in cases that the real time ocean conditions cause changes to origins, destinations, and physical routes. A vessel can use specific routes many times based on its origin and destination concerning historical vessel positions. Sometimes ships have to change course for different reasons such as adverse climatic agents, political disputes, or prohibitions; however, their trajectories are similar. Vessel traffic services save historical records to have evidence of the vessel's location. Industries such as marine traffic, whose system manages large amounts of navigational information for nearly 800 million position records per month, are an example of this. We define route as the vessel trajectory from the departure port to the arrival port. The position is one set of the coordinates in time of the vessel. The series of positions is all trajectory points of one vessel. Speed is the vessel's velocity. Course means the direction of the vessel's grades. One of the principal goals is the prediction and route estimation of vessel movements. It is a difficult task, however, because systems require high-performance computing. In addition, it requires historical information that can describe patterns in the flow of vessels. The inferential statistic has generated a significant number of tools to contribute to making scientific judgments on the uncertainty data.