Navigating Complexity: Automating Maritime Decision-Making with Temporal Transformer-Based Embeddings and Scalable Clustering
Bibliographic record
Abstract
The analysis of track data is fundamental to decision-making systems in domains such as maritime navigation, aviation, and logistics. Traditional reliance on handcrafted features and rule-based systems often struggles to scale with the increasing volume, variety, and complexity of data. This paper introduces an automated framework for maritime decision-making that leverages transformer-based embeddings and vector database technologies to overcome these limitations. Automatic Identification System (AIS) data, segmented into 12-hour chunks to optimize computational efficiency, is embedded into high-dimensional feature vectors using a time-series transformer. These embeddings are stored and queried in a scalable vector database, enabling efficient clustering and real-time anomaly detection. Extensive experiments validate the proposed system, demonstrating improvements in clustering accuracy, scalability, and classification performance. For instance, the Random Forest classifier achieved a weighted F1-score of 0.90, outperforming traditional handcrafted methods. Geospatial analysis confirmed the alignment of clustering results with real-world maritime patterns, underscoring the system's practical relevance. This work automates feature extraction, integrates advanced embedding and clustering techniques, and validates outputs through classification and annotation. The proposed framework not only addresses maritime challenges but also demonstrates adaptability to other domains, such as air traffic management and logistics. Future research will enhance clustering algorithms, incorporate predictive analytics, and extend the methodology to integrate multi-source data for broader applicability.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".