Abstract
The prediction of vessel maritime navigation has become an interesting topic in the last years, especially in areas of economical commercial exchange and security. Also, vessels monitoring requires better systems and techniques that help enterprises and governments to protect their interests. In specific, the prediction of vessels movements is important concerning safety and tracking. However, the applications of prediction techniques have a high cost of computational efficiency and low resource-saving. This article presents a sample method to select historical data on ship-specific routes to optimize the computational performance of the prediction of ship positions and route estimation in real-time. These historical navigation data can help us to estimate a complete path and perform vessel positions predictions through time. This method works in a vessel tracking system in order to save computational work when predictions or route estimations are in execution. The results obtained after testing the method are almost acceptable concerning route estimation with a precision of 74.98%, and with vessel positions predictions through time a 79% of accuracy.
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Bautista-Sánchez, R., Barbosa-Santillán, L.I., Sánchez-Escobar, J.J. (2019). Statistical Approach in Data Filtering for Prediction Vessel Movements Through Time and Estimation Route Using Historical AIS Data. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_3
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