Ship Trajectory Prediction Model Based on Improved Bi-LSTM
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 3
Abstract
Ship trajectory prediction plays an important role in ensuring ship safety; through accurate ship positioning, the future trajectory of ships and their encounter time and location can be obtained, which facilitates the maritime regulatory authorities to assess the risks of ship encounters and implement effective traffic control. Meanwhile, with the rapid development of the shipping industry, the increasingly complex maritime traffic poses potential risks, which may cause serious traffic accidents and huge economic losses. To improve the accuracy of ship navigation risk prediction and ensure the safety of ship navigation, automatic identification system (AIS) data and deep learning models are used to extract the ship trajectory change feature pattern and apply it to ship trajectory prediction. This study builds the improved bidirectional long short-term memory network (Bi-LSTM) model based on rectified adaptive moment estimation (Radam) and lookahead, respectively. The AIS data of the Port of Tianjin area were selected for model training, and the results of comparison experiments show that the improved Bi-LSTM model has a stronger generalization ability, which further improves the trajectory prediction accuracy, and shows excellent predictive performance. The prediction model is feasible for the prediction of ship navigation trajectory.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We gratefully acknowledge support from the Fundamental Research Funds for the Central Universities (Grant Nos. 3132023153 and 3132023154).
References
Alizadeh, D., A. A. Alesheikh, and M. Sharif. 2021. “Vessel trajectory prediction using historical automatic identification system data.” J. Navig. 74 (1): 156–174. https://doi.org/10.1017/S0373463320000442.
Bao, K., J. Bi, M. Gao, Y. Sun, X. Zhang, and W. Zhang. 2022. “An improved ship trajectory prediction based on AIS data using MHA-BiGRU.” J. Mar. Sci. Eng. 10 (6): 804. https://doi.org/10.3390/jmse10060804.
Billah, M. M., J. Zhang, and T. Zhang. 2022. “A method for vessel’s trajectory prediction based on encoder decoder architecture.” J. Mar. Sci. Eng. 10 (10): 1529. https://doi.org/10.3390/jmse10101529.
Cai, H., X. Chen, J. Ling, and Q. Xu. 2022. “Short-term load forecasting based on Radam optimized CNN-Bi-LSTM-AE hybrid model.” In Proc., 2022 Power System and Green Energy Conf. (PSGEC), 626–631. New York: IEEE.
Cui, K., Z. Zhan, and C. Pan. 2020. “Applying Radam method to improve treatment of convolutional neural network on banknote identification.” In Proc., 2020 Int. Conf. on Computer Engineering and Application (ICCEA), 468–476. New York: IEEE.
Gan, S., S. Liang, K. Li, J. Deng, and T. Cheng. 2016. “Ship trajectory prediction for intelligent traffic management using clustering and ANN.” In Proc., 2016 UKACC 11th Int. Conf. on Control (CONTROL), 1–6. New York: IEEE.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Hu, Y., W. Xia, X. X. Hu, H. Q. Sun, and Y. H. Wang. 2020. “Vessel trajectory prediction based on recurrent neural network.” Syst. Eng. Electron. 42 (4): 871–877. https://doi.org/10.3969/j.issn.1001-506X.2020.04.18.
Jiang, B., J. Guan, W. Zhou, and X. Chen. 2019. “Vessel trajectory prediction algorithm based on polynomial fitting Kalman filtering.” J. Signal Process. 5 (35): 741–746. https://doi.org/10.16798/j.issn.1003-0530.2019.05.002.
Li, W.-W., P.-T. Li, and L.-Y. Li. 2018. “Outliers recognition and the dimensionless method in comprehensive evaluation.” Oper. Res. Manage. Sci. 27 (4): 173–178.
Liang, Y., and H. Zhang. 2020. “Ship track prediction based on AIS data and PSO optimized LSTM network.” Int. Core J. Eng. 6 (5): 23–33. https://doi.org/10.6919/ICJE.202005_6(5).0005.
Liu, J., G. Shi, X. Yang, and Z. Kaige. 2020. “Ship trajectory prediction model based on DE-SVM.” J. Shanghai Marit. Univ. 41 (1): 34–39. https://doi.org/10.13340/j.jsmu.2020.01.006.
Liu, L., H. Jiang, P. He, W. Chen, X. Liu, J. Gao, and J. Han. 2019. “On the variance of the adaptive learning rate and beyond.” Preprint, submitted August 8, 2019. https://arxiv.org/abs/1908.03265.
Liu, X., H. Yuan, C. Xiao, Y. Wang, and Q. Yu. 2022. “Hybrid-driven vessel trajectory prediction based on uncertainty fusion.” Ocean Eng. 248 (Mar): 110836. https://doi.org/10.1016/j.oceaneng.2022.110836.
Mao, C. H., C. Pan, B. Yin, Y. N. Lu, and X. Q. Xu. 2017. “Ship navigation trajectory prediction based on Gaussian process regression.” Technol. Innovation Appl. 4 (31): 28–29.
Park, J., J. Jeong, and Y. Park. 2021. “Ship trajectory prediction based on Bi-LSTM using spectral-clustered AIS data.” J. Mar. Sci. Eng. 9 (9): 1037. https://doi.org/10.3390/jmse9091037.
Potter, C. 2020. “RNN based MIMO channel prediction.” In Vol. 4 of Differential evolution in electromagnetics. Evolutionary learning and optimization, edited by A. Qing and C. K. Lee, 177–206. Berlin: Springer.
Qian, L., Y. Zheng, L. Li, Y. Ma, C. Zhou, and D. Zhang. 2022. “A new method of inland water ship trajectory prediction based on long short-term memory network optimized by genetic algorithm.” Appl. Sci. 12 (8): 4073. https://doi.org/10.3390/app12084073.
Qiao, S., D. Shen, X. Wang, N. Han, and W. Zhu. 2014. “A self-adaptive parameter selection trajectory prediction approach via hidden Markov models.” IEEE Trans. Intell. Transp. Syst. 16 (1): 284–296. https://doi.org/10.1109/TITS.2014.2331758.
Quan, B., B.-C. Yang, K.-Q. Hu, C.-X. Guo, and Q.-Q. Li. 2018. “Prediction model of ship trajectory based on LSTM.” Comput. Sci. 45 (11A): 126–131.
Ran, Y., G. Shi, and W. Li. 2021. “Ship track prediction model based on automatic identification system data and bidirectional cyclic neural network.” In Proc., 2021 4th Int. Symp. on Traffic Transportation and Civil Architecture (ISTTCA), 297–301. New York: IEEE.
Rong, H., A. P. Teixeira, and C. G. Soares. 2019. “Ship trajectory uncertainty prediction based on a Gaussian process model.” Ocean Eng. 182 (Jun): 499–511. https://doi.org/10.1016/j.oceaneng.2019.04.024.
Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. “Learning representations by back-propagating errors.” Nature 323 (6088): 533–536. https://doi.org/10.1038/323533a0.
Sang, L.-Z., A. Wall, Z. Mao, X.-P. Yan, and J. Wang. 2015. “A novel method for restoring the trajectory of the inland waterway ship by using AIS data.” Ocean Eng. 110 (Dec): 183–194. https://doi.org/10.1016/j.oceaneng.2015.10.021.
Shu, Y., W. Daamen, H. Ligteringen, M. Wang, and S. Hoogendoorn. 2018. “Calibration and validation for the vessel maneuvering prediction (VMP) model using AIS data of vessel encounters.” Ocean Eng. 169 (Dec): 529–538. https://doi.org/10.1016/j.oceaneng.2018.09.022.
Su, Q. L. 2002. “Automatic identification system.” Mar. Electr. Electron. Eng. 2002 (6): 49–51.
Tang, H., Y. Yin, and H. Shen. 2022. “A model for vessel trajectory prediction based on long short-term memory neural network.” J. Mar. Eng. Technol. 21 (3): 136–145. https://doi.org/10.1080/20464177.2019.1665258.
Wan, Z., Z. Yuxiang, X. Gong, and B. Yu. 2021. “DenseNet model with RAdam optimization algorithm for cancer image classification.” In Proc., 2021 IEEE Int. Conf. on Consumer Electronics and Computer Engineering (ICCECE), 771–775. New York: IEEE.
Wang, H., Z. Liu, X. Wang, T. Graham, and J. Wang. 2021. “An analysis of factors affecting the severity of marine accidents.” Reliab. Eng. Syst. Saf. 210 (Jun): 107513. https://doi.org/10.1016/j.ress.2021.107513.
Wang, X., G. Xia, J. Zhao, J. Wang, Z. Yang, S. Loughney, S. Fang, S. Zhang, Y. Xing, and Z. Liu. 2023. “A novel method for the risk assessment of human evacuation from cruise ships in maritime transportation.” Reliab. Eng. Syst. Saf. 230 (Feb): 108887. https://doi.org/10.1016/j.ress.2022.108887.
Wu, B., Y. Tang, X. Yan, and C. G. Soares. 2021. “Bayesian network modelling for safety management of electric vehicles transported in RoPax ships.” Reliab. Eng. Syst. Saf. 209 (May): 107466. https://doi.org/10.1016/j.ress.2021.107466.
Xu, T., F. Cai, Q. Hu, and Y. Chun. 2014. “Research on estimation of AIS vessel trajectory data based on Kalman filter algorithm.” Mod. Electron. Tech. 5 (37): 97–100. https://doi.org/10.16652/j.issn.1004-373x.2014.05.007.
Xu, T., X. Liu, and X. Yang. 2011. “Ship Trajectory online prediction based on BP neural network algorithm.” In Vol. 1 of Proc., 2011 Int. Conf. of Information Technology, Computer Engineering and Management Sciences, 103–106. New York: IEEE.
Yang, C.-H., C.-H. Wu, J.-C. Shao, Y.-C. Wang, and C.-M. Hsieh. 2022. “AIS-based intelligent vessel trajectory prediction using Bi-LSTM.” IEEE Access 10 (Feb): 24302–24315. https://doi.org/10.1109/ACCESS.2022.3154812.
You, L., S. Xiao, Q. Peng, C. Claramunt, X. Han, Z. Guan, and J. Zhang. 2020. “ST-Seq2Seq: A spatio-temporal feature-optimized Seq2Seq model for short-term vessel trajectory prediction.” IEEE Access 8 (Dec): 218565–218574. https://doi.org/10.1109/ACCESS.2020.3041762.
Zhang, L., J. Zhang, J. Niu, Q. J. Wu, and G. Li. 2021. “Track prediction for HF radar vessels submerged in strong clutter based on MSCNN fusion with GRU-AM and AR model.” Remote Sens. 13 (11): 2164. https://doi.org/10.3390/rs13112164.
Zhang, M., J. Lucas, J. Ba, and G. E. Hinton. 2019. “Lookahead optimizer: k steps forward, 1 step back.” In Vol. 32 of Proc., Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.
Zhang, X., X. Fu, Z. Xiao, H. Xu, and Z. Qin. 2022. “Vessel trajectory prediction in maritime transportation: Current approaches and beyond.” IEEE Trans. Intell. Transp. Syst. 23 (11): 19980–19998. https://doi.org/10.1109/TITS.2022.3192574.
Zhao, J., J. Lu, X. Chen, Z. Yan, Y. Yan, and Y. Sun. 2022. “High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework.” Physica A 586 (Jan): 126470. https://doi.org/10.1016/j.physa.2021.126470.
Zhao, S.-B., C. Tang, S. Liang, and D.-J. Wang. 2012. “Track prediction of vessel in controlled waterway based on improved Kalman filter.” J. Comput. Appl. 32 (11): 3247–3250. https://doi.org/10.3724/SP.J.1087.2012.03247.
Zhao, Z., W. Chen, X. Wu, P. C. Chen, and J. Liu. 2017. “LSTM network: A deep learning approach for short-term traffic forecast.” IET Intel. Transport Syst. 11 (2): 68–75. https://doi.org/10.1049/iet-its.2016.0208.
Zhen, R., Y. X. Jin, Q. Y. Hu, C. J. Shi, and S. Z. Wang. 2017. “Vessel behavior prediction based on AIS data and BP neural network.” Navig. China 40 (2): 6–10.
Zheng, Y., X. Lv, L. Qian, and X. Liu. 2022. “An optimal BP neural network track prediction method based on a GA–ACO hybrid algorithm.” J. Mar. Sci. Eng. 10 (10): 1399. https://doi.org/10.3390/jmse10101399.
Zhou, X., Z. Zhang, and C. Zhang. 2021. “Bi-LSTM deep neural network reservoir classification model based on the innovative input of logging curve response sequences.” IEEE Access 9 (Jan): 19902–19915. https://doi.org/10.1109/ACCESS.2021.3053289.
Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 3 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Sep 13, 2023
Accepted: Jan 22, 2024
Published online: Apr 27, 2024
Published in print: Sep 1, 2024
Discussion open until: Sep 27, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.