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%.
Highlights
Maritime navigation is an essential part of international trade
Artificial Neural Networks (ANN) achieved the prediction of an interval of time and the estimation of the route using the Multivariate Imputation by Chained Equations (MICE) Algorithm
The PreMovEst method obtained the following results: Table 3 shows a contrast between the actual data of an interval of time and the ANN’s vessel movement predictions
Summary
Maritime navigation is an essential part of international trade. Global maritime exchange increased exponentially in 2017 [1], mainly driven by Asia and Europe’s economies since many countries export products around the world in large volumes. At the beginning of 2018, the merchant fleet was estimated to be 58,329 vessels [1], which led to more commercial exchange. It has led to the amount of maritime traffic increasing, it is necessary to analyze the information to improve the monitoring processes for different purposes. One main task is monitoring the seas to describe and predict moving through them, and, since e-commerce is growing exponentially, vessels are the primary mode of transport for products. Governments and enterprises are more interested in it. Data mining techniques (e.g., [2]) aim to discover patterns of movement of the ships from the delimitation of an area
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