Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-05-04T12:13:49.123Z Has data issue: false hasContentIssue false
  • English
  • Français

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: z.jiang@whut.edu.cn)
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Chu, X. M., Liu, T., Ma, F., Liu, X. L. and Zhong, M. (2014). Distribution characteristic of ais signal field intensity along mountainous waterway. Journal of Traffic & Transportation Engineering, 14(6), 117–126.Google Scholar
Chung, J., Gulcehre, C., Cho, K. H. and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. Eprint Arxiv. arXiv:1412.3555.Google Scholar
Contreras, J., Espinola, R., Nogales, F. J. and Conejo, A. J. (2003). Arima models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014–1020.Google Scholar
De Brébisson, A., Simon, É., Auvolat, A., Vincent, P. and Bengio, Y. (2015). Artificial neural networks applied to taxi destination prediction. Computer Science. arXiv:1508.00021.Google Scholar
Elman, J. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.Google Scholar
Gan, S., Liang, S., Li, K., Deng, J. and Cheng, T. (2018). Trajectory length prediction for intelligent traffic signaling: a data-driven approach. IEEE Transactions on Intelligent Transportation Systems, 19(2), 426–435.Google Scholar
Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. Journal of Machine Learning Research, 9, 249–256.Google Scholar
Goodwin, P. and Lawton, R. (1999). On the asymmetry of the symmetric map. International Journal of Forecasting, 15(4), 405–408.Google Scholar
Graves, A. (2012). Long Short-Term Memory. In: Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence, Vol. 385. Springer, Berlin, Heidelberg.Google Scholar
Harati-Mokhtari, A., Wall, A., Brooks, P. and Wang, J. (2007). Automatic identification system (AIS): data reliability and human error implications. The Journal of Navigation, 60(3), 373–389.Google Scholar
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.Google Scholar
International Telecommunication Union (ITU) (2014). Recommendation M.1371-5: Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band.Google Scholar
Jaskólski, K. (2014). The availability of automatic identification system (AIS) based on latency position reports in the gulf of gdansk. Annual of Navigation, 21(1), 59–74.Google Scholar
Jaskólski, K. (2013). Availability of ais binary data transmission based on dynamic measurements performed on the southern baltic and the danish straits. Annual of Navigation, 20(1), 25–36.Google Scholar
Kanarachos, S., Christopoulos, S. R. G., Chroneos, A. and Fitzpatrick, M. E. (2017). Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and hilbert transform. Expert Systems with Applications, 85, 292–304.Google Scholar
Kawan, B., Wang, H., Li, G. and Chhantyal, K. (2017). Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression. Conference on Simulation and Modelling, 350–354.Google Scholar
Khosroshahi, A., Ohn-Bar, E. and Trivedi, M. M. (2016). Surround vehicles trajectory analysis with recurrent neural networks. IEEE, International Conference on Intelligent Transportation Systems, 2267–2272.Google Scholar
Liu, C., Ling, J. and Kou, L. (2013). Performance comparison between ga-bp neural network and bp neural network. Chinese Journal of Health Statistics, 30(2), 173–494.Google Scholar
Liu, C., Zou, Z. J. and Li, T. S. (2015). Path following of underactuated surface vessels with fin roll reduction based on neural network and hierarchical sliding mode technique. Neural Computing & Applications, 26(7), 1525–1535.Google Scholar
Meijer, R. (2017). ETA prediction: Predicting the ETA of a container vessel based on route identification using AIS data. Master thesis, Delft University of Technology.Google Scholar
Rong, H. and Mou, J. (2013). Predict Maneuvering Indices Using AIS Data by Ridge Regression. International Workshop on Next Generation Nautical Traffic Models (pp. 102–111).Google Scholar
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.Google Scholar
Saxe, A. M., Mcclelland, J. L. and Ganguli, S. (2013). Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. Computer Science. arXiv:1312.6120.Google Scholar
Sang, L. Z., Wall, A., Mao, Z., Yan, X. P. and Wang, J. (2015). A novel method for restoring the trajectory of the inland waterway ship by using AIS data. Ocean Engineering, 110, 183–194.Google Scholar
Schuster, M. and Paliwal, K. K. (2002). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.Google Scholar
Song, H., Witt, S. F. and Jensen, T. C. (2003). Tourism forecasting: accuracy of alternative econometric models. International Journal of Forecasting, 19(1), 123–141.Google Scholar
Sun, C., Wu, C., Chu, D., Xie, L., Liu, L. and Li, H. (2016). Vehicle Trajectory Restoration Based on Vondrak Filtering and Cubic Spline Interpolation. Cota International Conference of Transportation Professionals, 235–248.Google Scholar
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.Google Scholar
Tian, L. Q. and Jia, J. (2016). Research of cubic spline interpolation in the ship track repair. Ship Science & Technology, 38(3), 79–81.Google Scholar
Wang, W. J., Liao, Y. F. and Chen, S. H. (2002). RNN-based prosodic modeling for mandarin speech and its application to speech-to-text conversion. Speech Communication, 36(3–4), 247–265.Google Scholar
Xu, T., Liu, X. and Yang, X. (2012). A novel approach for ship trajectory online prediction using BP neural network algorithm. Advances in Information Sciences & Service Sciences, 4(11), 271–277.Google Scholar
Zaremba, W., Sutskever, I. and Vinyals, O. (2014). Recurrent neural network regularization. Eprint Arxiv. arXiv:1409.2329Google Scholar
Zhang, W., Kopca, C., Tang, J., Ma, D. and Wang, Y. (2017). A systematic approach for collision risk analysis based on AIS data. The Journal of Navigation, 70(5), 1117–1132.Google Scholar