Open Access

ARTICLE

Ship Trajectory Prediction Based on BP Neural Network

Hai Zhou^1,2,*, Yaojie Chen^1,2, Sumin Zhang³

1 School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China.
2 Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, 430065, China.
3 School of Information Systems and Technology, Drexel University, Philadelphia, 19104, The United States.

*Corresponding Author: Hai Zhou. Email: .

Journal on Artificial Intelligence 2019, 1(1), 29-36. https://doi.org/10.32604/jai.2019.05939

Abstract

In recent years, with the prosperity of world trade, the water transport industry has developed rapidly, the number of ships has surged, and ship safety accidents in busy waters and complex waterways have become more frequent. Predicting the movement of the ship and analyzing the trajectory of the ship are of great significance for improving the safety level of the ship. Aiming at the multi-dimensional characteristics of ship navigation behavior and the accuracy and real-time requirements of ship traffic service system for ship trajectory prediction, a ship navigation trajectory prediction method combining ship automatic identification system information and Back Propagation (BP) neural network are proposed. According to the basic principle of BP neural network structure, the BP neural network is trained by taking the characteristic values of ship navigation behavior at three consecutive moments as input and the characteristic values of ship navigation behavior at the fourth moment as output to predict the future ship navigation trajectory. Based on the Automatic Identification System (AIS) information of the waters near the Nanpu Bridge in Pudong New Area, Shanghai, the results show that the method is used to predict the ship's navigational behavior eigenvalues accurately and in real time. Compared with the traditional kinematics prediction trajectory method, the model can effectively predict ship navigation. The trajectory improves the accuracy of the ship's motion situation prediction, and has the advantages of high computational efficiency and strong versatility, and the error is within an acceptable range.

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Citations