{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:06:35Z","timestamp":1768676795211,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In complex marine environments, intelligent vessels require a high level of dynamic perception to process multiple types of information for mitigating collision risks. To ensure the safety of maritime traffic and enhance the efficiency of navigation information, vessel trajectory prediction is crucial for Automatic Identification Systems (AIS). This study introduces a Graph Convolutional Mamba Network (GC-MT) utilizing AIS data for predicting vessel trajectories. To capture motion interaction characteristics, we employed a Graph Convolutional Network (GCN) to construct a spatiotemporal graph that reflects the interaction relationships among various vessels within the maritime information flow. Furthermore, high-level spatiotemporal features were extracted using a Mamba Neural Network (MNN) to incorporate time-related dynamics. Validation against real-world historical AIS data demonstrates that the proposed model achieved improvements of approximately 35% and 28% in the Average Displacement Error (ADE) and Final Displacement Error (FDE), respectively, compared to the leading baseline model. The predictive capability of the proposed method demonstrates its effectiveness in improving maritime navigation safety in a shipping environment with multiple information sources.<\/jats:p>","DOI":"10.3390\/info16040311","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T07:42:01Z","timestamp":1744616521000},"page":"311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7825-9835","authenticated-orcid":false,"given":"Haixiong","family":"Ye","sequence":"first","affiliation":[{"name":"College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5339-8329","authenticated-orcid":false,"given":"Xiliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107140","DOI":"10.1016\/j.cie.2021.107140","article-title":"Ship\u2019s Response to Low-Sulfur Regulations: From the Perspective of Route, Speed and Refueling Strategy","volume":"155","author":"Ma","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Guo, S., Mou, J., Chen, L., and Chen, P. (2021). An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9060609"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, Y., Li, B., and Qi, Z. (2022). Ship Abnormal Behavior Detection Method Based on Optimized GRU Network. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10020249"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fossen, S., and Fossen, T.I. (2018, January 20\u201322). Extended Kalman Filter Design and Motion Prediction of Ships Using Live Automatic Identification System (Ais) Data. Proceedings of the 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS), Bern, Switzerland.","DOI":"10.1109\/EECS.2018.00092"},{"key":"ref_5","first-page":"71","article-title":"Automatic Identification System (AIS) Dynamic Data Estimation Based on Discrete Kalman Filter (KF) Algorithm","volume":"58","year":"2017","journal-title":"Zesz. Nauk. Akad. Mar. Wojennej"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, X., Liu, G., Hu, C., and Ma, X. (2019, January 27\u201330). Wavelet Analysis Based Hidden Markov Model for Large Ship Trajectory Prediction. Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China.","DOI":"10.23919\/ChiCC.2019.8866006"},{"key":"ref_7","unstructured":"Luo, X., Wang, J., Li, J., Lu, H., Lai, Q., and Zhu, X. (, January 29\u201331). Research on Ship Trajectory Prediction Using Extended Kalman Filter and Least-Squares Support Vector Regression Based on AIS Data. Proceedings of the 2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021): Beijing, China."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.oceaneng.2019.04.024","article-title":"Ship Trajectory Uncertainty Prediction Based on a Gaussian Process Model","volume":"182","author":"Rong","year":"2019","journal-title":"Ocean. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"118303","DOI":"10.1016\/j.oceaneng.2024.118303","article-title":"Enhancing Short-Term Vessel Trajectory Prediction with Clustering for Heterogeneous and Multi-Modal Movement Patterns","volume":"308","author":"Alam","year":"2024","journal-title":"Ocean. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Guo, S., Liu, C., Guo, Z., Feng, Y., Hong, F., and Huang, H. (2018). Trajectory Prediction for Ocean Vessels Base on K-Order Multivariate Markov Chain. Wireless Algorithms, Systems, and Applications, Proceedings of the 13th International Conference, WASA 2018, Tianjin, China, 20\u201322 June 2018, Springer International Publishing.","DOI":"10.1007\/978-3-319-94268-1_12"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xiaopeng, T., Xu, C., Lingzhi, S., Zhe, M., and Qing, W. (2015, January 25\u201328). Vessel Trajectory Prediction in Curving Channel of Inland River. Proceedings of the 2015 International Conference on Transportation Information and Safety (ICTIS), Wuhan, China.","DOI":"10.1109\/ICTIS.2015.7232156"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, M., Huang, L., Wen, Y., Zhang, J., Huang, Y., and Zhu, M. (2022). Short-Term Trajectory Prediction of Maritime Vessel Using k-Nearest Neighbor Points. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10121939"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6622862","DOI":"10.1155\/2022\/6622862","article-title":"Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data","volume":"2022","author":"Ma","year":"2022","journal-title":"J. Adv. Transp."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4329","DOI":"10.1109\/TAES.2021.3096873","article-title":"Deep Learning Methods for Vessel Trajectory Prediction Based on Recurrent Neural Networks","volume":"57","author":"Capobianco","year":"2021","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Ni, G., and Xu, Y. (2020, January 12\u201314). Ship Trajectory Prediction Based on LSTM Neural Network. Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.","DOI":"10.1109\/ITOEC49072.2020.9141702"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Forti, N., Millefiori, L.M., Braca, P., and Willett, P. (2020, January 4\u20138). Prediction Oof Vessel Trajectories from AIS Data via Sequence-to-Sequence Recurrent Neural Networks. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054421"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Suo, Y., Chen, W., Claramunt, C., and Yang, S. (2020). A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network. Sensors, 20.","DOI":"10.3390\/s20185133"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"19980","DOI":"10.1109\/TITS.2022.3192574","article-title":"Vessel Trajectory Prediction in Maritime Transportation: Current Approaches and Beyond","volume":"23","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"117431","DOI":"10.1016\/j.oceaneng.2024.117431","article-title":"G-Trans: A Hierarchical Approach to Vessel Trajectory Prediction with GRU-Based Transformer","volume":"300","author":"Xue","year":"2024","journal-title":"Ocean. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Donandt, K., and S\u00f6ffker, D. (2024, January 7\u201311). Incorporating Navigation Context into Inland Vessel Trajectory Prediction: A Gaussian Mixture Model and Transformer Approach. Proceedings of the 2024 27th International Conference on Information Fusion (FUSION), Venice, Italy.","DOI":"10.23919\/FUSION59988.2024.10706281"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.joes.2021.03.001","article-title":"Ship Behavior Prediction via Trajectory Extraction-Based Clustering for Maritime Situation Awareness","volume":"7","author":"Murray","year":"2022","journal-title":"J. Ocean. Eng. Sci."},{"key":"ref_22","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_23","unstructured":"Wu, L., Sun, P., Hong, R., Fu, Y., Wang, X., and Wang, M. (2018). SocialGCN: An Efficient Graph Convolutional Network Based Model for Social Recommendation. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1162\/neco_a_01046","article-title":"Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells","volume":"30","author":"Voelker","year":"2018","journal-title":"Neural Comput."},{"key":"ref_26","unstructured":"Gu, A., and Dao, T. (2023). Mamba: Linear-Time Sequence Modeling with Selective State Spaces 2024. arXiv."},{"key":"ref_27","unstructured":"Agarap, A.F. (2018). Deep Learning Using Rectified Linear Units (ReLU) 2019. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Iserles, A. (2009). A First Course in the Numerical Analysis of Differential Equations, Cambridge University Press.","DOI":"10.1017\/CBO9780511995569"},{"key":"ref_29","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian Error Linear Units (GELUs) 2023. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pellegrini, S., Ess, A., Schindler, K., and Van Gool, L. (October, January 27). You\u2019ll Never Walk Alone: Modeling Social Behavior for Multi-Target Tracking. Proceedings of the 2009 IEEE 12th international conference on computer vision, Kyoto, Japan.","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"ref_31","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., and Savarese, S. (July, January 26). Social Lstm: Human Trajectory Prediction in Crowded Spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1109\/JIOT.2019.2948075","article-title":"Mobility Predictions for IoT Devices Using Gated Recurrent Unit Network","volume":"7","author":"Adege","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., and Claudel, C. (2020, January 13\u201319). Social-Stgcnn: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"ref_34","unstructured":"Loshchilov, I., and Hutter, F. (2019). Decoupled Weight Decay Regularization. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/311\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:14:13Z","timestamp":1760030053000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/4\/311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,14]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["info16040311"],"URL":"https:\/\/doi.org\/10.3390\/info16040311","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,14]]}}}