GL‐LoiterDNet: A Hybrid Model for Ship Trajectory Prediction in Loitering Activity Scenarios
Bibliographic record
Abstract
In loitering activity scenarios, vessels frequently execute course changes within localized maritime spaces, often exhibiting extreme turning maneuvers that generate ultralong, dense, and highly nonlinear spatiotemporal trajectories. Traditional prediction models demonstrate limitations in processing dynamically changing trajectory features, leading to insufficient prediction accuracy under such loitering conditions. To address this challenge, this study proposes a GL‐LoiterDNet, a hybrid deep learning–based vessel trajectory prediction model. The model incorporates multidimensional trajectory characterization features including speed fluctuations, navigational positions, and course differentials. It integrates 1D convolutional neural networks (1D‐CNN), bidirectional gated recurrent units (BiGRU), and bidirectional long short‐term memory (BiLSTM) networks to capture both localized abrupt variations and long‐term evolutionary patterns in loitering trajectories, thereby mitigating feature degradation phenomena. Experimental validation using trajectory data from vessels in Sagami Bay, Japan, demonstrates that the GL‐LoiterDNet model outperforms 14 baseline models in prediction accuracy and robustness. The model exhibits rolling multistep trajectory prediction capability for loitering scenarios, achieving an average positioning error below 0.7 km within 10‐min prediction windows. This research can provide reliable theoretical and data‐driven support for continuous vessel positioning and monitoring in complex maritime operation scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".