Abstract
Due to the rapid economic development of modern society, the demand for cargo in the shipping industry has experienced unprecedented growth in recent years. The introduction of a large number of ships, especially large, new, and intelligent ships, has made shipping networks more complex. Controlling transportation risks has become more challenging than ever before. Ship trajectory prediction based on automatic identification system (AIS) data can effectively help identify abnormal ship behaviors and reduce maritime risks such as collisions, grounding, and contacts. In recent years, with the rapid development of deep learning theories, recurrent neural network models (long short-term memory and gated recurrent unit) have been widely used in ship trajectory prediction due to their powerful ability to capture hidden information in time-series data. However, these models struggle with tasks involving high complexity of trajectory features. To address this issue, this paper introduces a switching-input mechanism based on LSTM, constructing a ship trajectory prediction model based on the SI-LSTM model. The switching-input mechanism enables the model to adjust its processing of important information according to dynamic changes in input data, effectively capturing local features of complex trajectories. The experimental section, which includes eight cases of complex trajectories, demonstrates the competitive generalization ability and prediction accuracy of SI-LSTM.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The data and code of Switching-input LSTM Network can be found at https://github.com/1101Floor/SI-LSTM.
References
Zhang X, Fu X, Xiao Z et al (2023) 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
Liu RW, Hu K, Liang M et al (2023) QSD-LSTM: Vessel trajectory prediction using long short-term memory with quaternion ship domain. Appl Ocean Res 136:103592. https://doi.org/10.1016/j.apor.2023.103592
Zhi L, Zuo Y (2024) Collaborative path planning of multiple AUVs based on adaptive Multi-population PSO. J Mar Sci Eng 12(2):223. https://doi.org/10.3390/jmse12020223
Li HH, Yang Z (2023) Towards safe navigation environment: the imminent role of spatio-temporal pattern mining in maritime piracy incidents analysis. Reliab Eng Syst Saf 238:109422. https://doi.org/10.1016/j.ress.2023.109422
Cai M, Zhang J, Zhang D et al (2021) Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data. Reliab Eng Syst Saf 215:107901. https://doi.org/10.1016/j.ress.2021.107901
Bakdi A, Glad IK, Vanem E (2020) AIS-Based multiple vessel collision and grounding risk identification based on Adaptive Safety Domain. J Mar Sci Eng 8(1):5. https://doi.org/10.3390/jmse8010005
Mou JM, Tak Cvd, Ligteringen H (2010) Study on collision avoidance in busy waterways by using AIS data. Ocean Eng 37(5):483–490. https://doi.org/10.1016/j.oceaneng.2010.01.012
Jiang JH, Zuo Y (2024) STIA-DJANet: spatial–temporal intention-aware vessel trajectory prediction based on dual-joint attention network for e-navigation. Expert Syst Appl 262:125550. https://doi.org/10.1016/j.eswa.2024.125550
Li HH, Yang Z (2023) Incorporation of AIS data-based machine learning into unsupervised route planning for maritime autonomous surface ships. Transp Res E 176:103171. https://doi.org/10.1016/j.tre.2023.103171
Xiao Z, Fu X, Zhang L (2022) Big data driven vessel trajectory and navigating state prediction with adaptive learning, motion modeling and particle filtering techniques. IEEE Trans Intell Transp Syst 23(4):3696–3709. https://doi.org/10.1109/TITS.2020.3040268
Jiang J, Zuo Y, Xiao Y et al (2024) STMGF-Net: a spatiotemporal multi-graph fusion network for vessel trajectory forecasting in intelligent maritime navigation. IEEE Trans Intell Transp Syst 1–13. https://doi.org/10.1109/TITS.2024.3465234
Chen X, Ling J, Yang Y (2020) Math Probl Eng. https://doi.org/10.1155/2020/7191296. Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction
Tang H, Yin Y, Shen H (2019) A model for vessel trajectory prediction based on long short-term memory neural network. J Mar Eng Technol 21:136–145. https://doi.org/10.1080/20464177.2019.1665258
Ma X, Tao Z, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C: Emerg Technol 54:187–197. https://doi.org/10.1016/j.trc.2015.03.014
Abebe M, Noh Y, Kang YJ (2022) Ship trajectory planning for collision avoidance using hybrid ARIMA-LSTM models. Ocean Eng 256:111527. https://doi.org/10.1016/j.oceaneng.2022.111527
Zhang K, Huang L, He Y (2023) A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty. Ocean Eng 278:114205. https://doi.org/10.1016/j.oceaneng.2023.114205
Ding M, Su W, Liu Y (2020) A Novel Approach on Vessel Trajectory Prediction Based on Variational LSTM. In: ICAICA, Dalian, China, June 27–29, 2020, pp. 206–211
Dey RK, Das AK (2023) Modified term frequency-inverse document frequency based deep hybrid framework for sentiment analysis. Multimedia Tools Appl 82(32):32967–32990. https://doi.org/10.1007/s11042-023-14653-1
Li X, Ma Y, Zhu JJ (2021) An online dual filters RUL prediction method of lithium-ion battery based on unscented particle filter and least squares support vector machine. Measurement 184:109935. https://doi.org/10.1016/j.measurement.2021.109935
Lipton Z, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019
Mohammadi M, Talebpour F, Safaee E et al (2018) Small-scale building load forecast based on hybrid forecast engine. Neural Process Lett 48(1):329–351. https://doi.org/10.1007/s11063-017-9723-2
Slaughter I, Charla JL, Siderius M (2024) Vessel trajectory prediction with recurrent neural networks: an evaluation of datasets, features, and architectures. J Ocean Eng Sci 8(39):1–10. https://doi.org/10.1016/j.joes.2024.01.002
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Cornputation 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Felix A, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Cornputation 12(10):2451–2471. https://doi.org/10.1049/cp:19991218
Dey RK, Das AK (2024) Neighbour adjusted dispersive flies optimization based deep hybrid sentiment analysis framework. Multimedia Tools Appl 83(3):64393–64416. https://doi.org/10.1007/s11042-023-17953-8
Xiao Z, Xu X, Xing H (2021) RTFN: a robust temporal feature network for time series classification. Inf Sci 571:65–86. https://doi.org/10.1016/j.ins.2021.04.053
Liu F, Zhou X, Cao J (2022) Anomaly Detection in Quasi-periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN. IEEE Trans Knowl Data Eng 34(6):2626–2640. https://doi.org/10.1109/TKDE.2020.3014806
Wang Z, Liu N, Chen C et al (2023) Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries. Inf Sci 635:398–413. https://doi.org/10.1016/j.ins.2023.01.100
Zhang X, Liu J, Gong P (2023) Trajectory prediction of seagoing ships in dynamic traffic scenes via a gated spatio-temporal graph aggregation network. Ocean Eng 287:115886. https://doi.org/10.1016/j.oceaneng.2023.115886
Chung J, Gülçehre Ç, Cho K (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv preprint arXiv:1412.3555
Bao KX, Bi JQ, Gao M et al (2022) An Improved Ship Trajectory Prediction based on AIS Data using MHA-BiGRU. J Mar Sci Eng 10:2077–1312. https://doi.org/10.3390/jmse10060804
Li HH, Jiao H, Yang ZL et al (2023) AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods. Transp Res E 175:103152. https://doi.org/10.1016/j.tre.2023.103152
Xue HQ, Wang S, Xia ML et al (2024) G-Trans: a hierarchical approach to vessel trajectory prediction with GRU-based transformer. Ocean Eng 300:117431. https://doi.org/10.1016/j.oceaneng.2024.117431
Venskus J, Treigys P, Markeviuciute J (2021) Unsupervised marine vessel trajectory prediction using LSTM network and wild bootstrapping techniques. Nonlinear Analysis: Modelling Control 26:718–737. https://doi.org/10.15388/namc.2021.26.23056
Last P, Bahlke C, Hering-Bertram M et al (2014) Comprehensive analysis of automatic identification system (AIS) data in regard to vessel movement prediction. J Navig 67(5):791–809. https://doi.org/10.1017/S0373463314000253
Zhang D, Jia L, Wu Q Enhance the AIS data availability by screening and interpolation. In: 2017 4th International Conference on Transportation Information and Safety, Banff, AB, Canada et al (2017) August 08–10, 2017, https://doi.org/10.1109/ICTIS.2017.8047888
Hu HB, Zheng X, Yin J (2021) Research on O-ring dimension measurement Algorithm based on cubic spline interpolation. Appl Sci 11(8):3716. https://doi.org/10.3390/app11083716
Li H, Jiao H, Yang Z (2023) Ship trajectory prediction based on machine learning and deep learning: a systematic review and methods analysis. Eng Appl Artif Intell 126:107062. https://doi.org/10.1016/j.engappai.2023.107062
Xiao Z, Xing H, Qu R et al (2024) Densely Knowledge-Aware Network for Multivariate Time Series classification. IEEE Trans Syst Man Cybernetics: Syst 54(4):2192–2204. https://doi.org/10.1109/TSMC.2023.3342640
Xiao Z, Xu X, Xing H et al (2024) DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series classification. IEEE Trans Cogn Dev Syst 16(4):1445–1459. https://doi.org/10.1109/TCDS.2024.3370219
Xu ZH, Lv ZQ, Chu BJ et al (2024) A fast matrix autoregression algorithm based on Tucker decomposition for online prediction of nonlinear real-time taxi-hailing demand without pre-training. Chaos Solitons Fractals 189:115660. https://doi.org/10.1016/j.chaos.2024.115660
Li BY, Yang YH, Zhao ZY et al (2024) A Novel Ensemble Learning Approach for Intelligent Logistics demand management. J Internet Technol 25(4):507–515. https://doi.org/10.70003/160792642024072504002
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (grant nos. 52131101, 51939001) and the Science and Technology Fund for Distinguished Young Scholars of Dalian (grant no. 2021RJ08).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, W., Yi, Z., Zhao, L. et al. Application of switching-input LSTM network for vessel trajectory prediction. Appl Intell 55, 289 (2025). https://doi.org/10.1007/s10489-024-06079-5
Accepted:
Published:
DOI: https://doi.org/10.1007/s10489-024-06079-5