AIS (Automatic Identification System) is a mandatory navigational equipment on board according to SOLAS (Safety of Life at Sea) convention. It is an automatic tracking system that uses VHF (Very High Frequency) transponders on ships and is used by VTS (Vessel Traffic Services) for monitor vessel movements. Existing AIS data has some principle defects due to radio propagation. This paper provides an approach to predict ship behavior with AIS data. In order to solve the problem that traditional ship behavior prediction needs to establish complex ship motion model, a new ship behavior prediction method based on LSTM (Long Short-Term Memory, LSTM) neural network model of machine learning is proposed. LSTM is the optimization model of RNN (Recurrent Neural Networks). Unlike standard feedback neural networks, LSTM has feedback connections. It can not only process single data points, but also entire sequences of data. These prominent features just match the characteristics of AIS data. The LSTM neural network prediction model is established and the shore is used. Based on the real data of AIS (Automatic Identification System) which ships engaged in the waters of South China Sea, the time series of ship behavior characteristics are extracted to train the model and validate the data. The training data is grouped by MMSI (Maritime Mobile Service Identity) and ensure the equal interval requirements of the ship's navigation behavior sequence data. This paper presents 4 figures with the parameter course, speed, position and the loss curve of LSTM training and testing. The results show that the model has a high accuracy and avoids the complicated process of ship motion modeling. The predicted results can improve the supervision of VTS (Vessel Traffic Services) and play a high practical application value in early warning of ship collision, SAR (Search and Rescue) operation and safety-related issues.
| Published in | International Journal of Transportation Engineering and Technology (Volume 6, Issue 1) |
| DOI | 10.11648/j.ijtet.20200601.13 |
| Page(s) | 16-21 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Ship Behavior, Prediction, AIS, LSTM, RNN, Machine Learning
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APA Style
Sun Yang, Peng Xinya, Ding Zexuan, Zhao Jiansen. (2020). An Approach to Ship Behavior Prediction Based on AIS and RNN Optimization Model. International Journal of Transportation Engineering and Technology, 6(1), 16-21. https://doi.org/10.11648/j.ijtet.20200601.13
ACS Style
Sun Yang; Peng Xinya; Ding Zexuan; Zhao Jiansen. An Approach to Ship Behavior Prediction Based on AIS and RNN Optimization Model. Int. J. Transp. Eng. Technol. 2020, 6(1), 16-21. doi: 10.11648/j.ijtet.20200601.13
AMA Style
Sun Yang, Peng Xinya, Ding Zexuan, Zhao Jiansen. An Approach to Ship Behavior Prediction Based on AIS and RNN Optimization Model. Int J Transp Eng Technol. 2020;6(1):16-21. doi: 10.11648/j.ijtet.20200601.13
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Download@article{10.11648/j.ijtet.20200601.13,
author = {Sun Yang and Peng Xinya and Ding Zexuan and Zhao Jiansen},
title = {An Approach to Ship Behavior Prediction Based on AIS and RNN Optimization Model},
journal = {International Journal of Transportation Engineering and Technology},
volume = {6},
number = {1},
pages = {16-21},
doi = {10.11648/j.ijtet.20200601.13},
url = {https://doi.org/10.11648/j.ijtet.20200601.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20200601.13},
abstract = {AIS (Automatic Identification System) is a mandatory navigational equipment on board according to SOLAS (Safety of Life at Sea) convention. It is an automatic tracking system that uses VHF (Very High Frequency) transponders on ships and is used by VTS (Vessel Traffic Services) for monitor vessel movements. Existing AIS data has some principle defects due to radio propagation. This paper provides an approach to predict ship behavior with AIS data. In order to solve the problem that traditional ship behavior prediction needs to establish complex ship motion model, a new ship behavior prediction method based on LSTM (Long Short-Term Memory, LSTM) neural network model of machine learning is proposed. LSTM is the optimization model of RNN (Recurrent Neural Networks). Unlike standard feedback neural networks, LSTM has feedback connections. It can not only process single data points, but also entire sequences of data. These prominent features just match the characteristics of AIS data. The LSTM neural network prediction model is established and the shore is used. Based on the real data of AIS (Automatic Identification System) which ships engaged in the waters of South China Sea, the time series of ship behavior characteristics are extracted to train the model and validate the data. The training data is grouped by MMSI (Maritime Mobile Service Identity) and ensure the equal interval requirements of the ship's navigation behavior sequence data. This paper presents 4 figures with the parameter course, speed, position and the loss curve of LSTM training and testing. The results show that the model has a high accuracy and avoids the complicated process of ship motion modeling. The predicted results can improve the supervision of VTS (Vessel Traffic Services) and play a high practical application value in early warning of ship collision, SAR (Search and Rescue) operation and safety-related issues.},
year = {2020}
}
TY - JOUR T1 - An Approach to Ship Behavior Prediction Based on AIS and RNN Optimization Model AU - Sun Yang AU - Peng Xinya AU - Ding Zexuan AU - Zhao Jiansen Y1 - 2020/02/28 PY - 2020 N1 - https://doi.org/10.11648/j.ijtet.20200601.13 DO - 10.11648/j.ijtet.20200601.13 T2 - International Journal of Transportation Engineering and Technology JF - International Journal of Transportation Engineering and Technology JO - International Journal of Transportation Engineering and Technology SP - 16 EP - 21 PB - Science Publishing Group SN - 2575-1751 UR - https://doi.org/10.11648/j.ijtet.20200601.13 AB - AIS (Automatic Identification System) is a mandatory navigational equipment on board according to SOLAS (Safety of Life at Sea) convention. It is an automatic tracking system that uses VHF (Very High Frequency) transponders on ships and is used by VTS (Vessel Traffic Services) for monitor vessel movements. Existing AIS data has some principle defects due to radio propagation. This paper provides an approach to predict ship behavior with AIS data. In order to solve the problem that traditional ship behavior prediction needs to establish complex ship motion model, a new ship behavior prediction method based on LSTM (Long Short-Term Memory, LSTM) neural network model of machine learning is proposed. LSTM is the optimization model of RNN (Recurrent Neural Networks). Unlike standard feedback neural networks, LSTM has feedback connections. It can not only process single data points, but also entire sequences of data. These prominent features just match the characteristics of AIS data. The LSTM neural network prediction model is established and the shore is used. Based on the real data of AIS (Automatic Identification System) which ships engaged in the waters of South China Sea, the time series of ship behavior characteristics are extracted to train the model and validate the data. The training data is grouped by MMSI (Maritime Mobile Service Identity) and ensure the equal interval requirements of the ship's navigation behavior sequence data. This paper presents 4 figures with the parameter course, speed, position and the loss curve of LSTM training and testing. The results show that the model has a high accuracy and avoids the complicated process of ship motion modeling. The predicted results can improve the supervision of VTS (Vessel Traffic Services) and play a high practical application value in early warning of ship collision, SAR (Search and Rescue) operation and safety-related issues. VL - 6 IS - 1 ER -
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