A Hybrid Prediction Model Based on KNN-LSTM for Vessel Trajectory

Lixiang Zhang, Yian Zhu (), Jiang Su, Wei Lu, Jiayu Li and Ye Yao
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Lixiang Zhang: School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Yian Zhu: School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Jiang Su: School of Software, Northwestern Polytechnical University, Xi’an 710072, China
Wei Lu: School of Information, Xi’an University of Finance and Economics, Xi’an 710100, China
Jiayu Li: Queen Mary University of London Engineering School, Northwestern Polytechnical University, Xi’an 710072, China
Ye Yao: School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China

Mathematics, 2022, vol. 10, issue 23, 1-20

Abstract: Trajectory prediction technology uses the trajectory data of historical ships to predict future ship trajectory, which has significant application value in the field of ship driving and ship management. With the popularization of Automatic Identification System (AIS) equipment in-stalled on ships, many ship trajectory data are collected and stored, providing a data basis for ship trajectory prediction. Currently, most of the ship trajectory prediction methods do not fully consider the influence of ship density in different sea areas, leading to a large difference in the prediction effect in different sea areas. This paper proposes a hybrid trajectory prediction model based on K-Nearest Neighbor (KNN) and Long Short-Term Memory (LSTM) methods. In this model, different methods are used to predict trajectory based on trajectory density. For offshore waters with a high density of trajectory, an optimized K-Nearest Neighbor algorithm is used for prediction. For open sea waters with low density of trajectory, the Long Short-Term Memory model is used for prediction. To further improve the prediction effect, the spatio-temporal characteristics of the trajectory are fully considered in the prediction process of the model. The experimental results for the dataset of historical data show that the mean square error of the proposed method is less than 2.92 × 10 −9 . Compared to the prediction methods based on the Kalman filter, the mean square error decreases by two orders of magnitude. Compared to the prediction methods based on recurrent neural network, the mean square error decreases by 82%. The advantage of the proposed model is that it can always obtain a better prediction result under different conditions of trajectory density available for different sea areas.

Keywords: LSTM neural network; KNN; trajectory prediction; automatic recognition system; sea area division (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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