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Inland Ship Trajectory Restoration by Recurrent Neural Network

Published online by Cambridge University Press:  17 May 2019

Cheng Zhong ,
Zhonglian Jiang ,
Xiumin Chu  and
Lei Liu
Cheng Zhong
Affiliation:
(National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China)
Zhonglian Jiang*
Affiliation:
(National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China) (Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, Chongqing 400074, China)
Xiumin Chu
Affiliation:
(National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China) (Marine Intelligent Ship Engineering Research Center of Fujian Province Colleges and Universities, Minjiang University, Fuzhou 350108, China)
Lei Liu
Affiliation:
(National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China)
*
(E-mail: [email protected])
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Abstract

The quality of Automatic Identification System (AIS) data is of fundamental importance for maritime situational awareness and navigation risk assessment. To improve operational efficiency, a deep learning method based on Bi-directional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs) is proposed and applied in AIS trajectory data restoration. Case studies have been conducted in two distinct reaches of the Yangtze River and the capability of the proposed method has been evaluated. Comparisons have been made between the BLSTM-RNNs-based method and the linear method and classic Artificial Neural Networks. Satisfactory results have been obtained by all methods in straight waterways while the BLSTM-RNNs-based method is superior in meandering waterways. Owing to the bi-directional prediction nature of the proposed method, ship trajectory restoration is favourable for complicated geometry and multiple missing points cases. The residual error of the proposed model is computed through Euclidean distance which decreases to an order of 10 m. It is considered that the present study could provide an alternative method for improving AIS data quality, thus ensuring its completeness and reliability.

Keywords

Inland waterwayAIS dataData restorationRecurrent Neural Networks
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 6 , November 2019 , pp. 1359 - 1377
Copyright
Copyright © The Royal Institute of Navigation 2019 

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