GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions
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 cruci...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/4/311 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849713859028320256 |
|---|---|
| author | Haixiong Ye Wei Wang Xiliang Zhang |
| author_facet | Haixiong Ye Wei Wang Xiliang Zhang |
| author_sort | Haixiong Ye |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1a5673ee1a0f4d39b77fded6ab1cf030 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-1a5673ee1a0f4d39b77fded6ab1cf0302025-08-20T03:13:51ZengMDPI AGInformation2078-24892025-04-0116431110.3390/info16040311GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine RegionsHaixiong Ye0Wei Wang1Xiliang Zhang2College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaSchool of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, ChinaIn 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.https://www.mdpi.com/2078-2489/16/4/311time-series forecastingdeep learningvessel trajectory predictionGraph Convolutional NetworkAutomatic Identification SystemSelective State Space Model |
| spellingShingle | Haixiong Ye Wei Wang Xiliang Zhang GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions Information time-series forecasting deep learning vessel trajectory prediction Graph Convolutional Network Automatic Identification System Selective State Space Model |
| title | GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions |
| title_full | GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions |
| title_fullStr | GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions |
| title_full_unstemmed | GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions |
| title_short | GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions |
| title_sort | gc mt a novel vessel trajectory sequence prediction method for marine regions |
| topic | time-series forecasting deep learning vessel trajectory prediction Graph Convolutional Network Automatic Identification System Selective State Space Model |
| url | https://www.mdpi.com/2078-2489/16/4/311 |
| work_keys_str_mv | AT haixiongye gcmtanovelvesseltrajectorysequencepredictionmethodformarineregions AT weiwang gcmtanovelvesseltrajectorysequencepredictionmethodformarineregions AT xiliangzhang gcmtanovelvesseltrajectorysequencepredictionmethodformarineregions |