A systematic review of cognitive and social factors in vessel traffic services operations
Bibliographic record
Abstract
Vessel traffic service (VTS) plays a key role in the safety of maritime navigation by organising the sea traffic, ensuring regulatory compliance, promoting information exchange and early detection of navigational hazards and assisting in collision avoidance. The cognitive and social factors influencing the performance of VTS operators require important considerations in this regard. Current developments in the maritime industry and changing operational profiles present novel challenges for VTS operators. This study aims to present the empirical findings related to the applied cognitive and social factors pertaining to VTS operations for the past two decades. A systematic literature review was conducted with a Boolean search strategy across six major databases. The literature associated with empirical investigations was extracted as per the PRISMA guidelines. The study identified 19 articles that satisfied the pre-determined inclusion criteria. A qualitative synthesis of the identified literature was performed, aggregating the findings into various sub-groups based on thematic areas and contexts. The obtained results revealed fatigue and mental workload as the most frequently examined factors, while factors such as decision-making, communication, coordination and perception also influenced the VTS operator’s performance. The findings shed light on the current state of the art for research and practical applications related to cognitive and social factors influencing VTS operator performance and their impact on maritime safety. The result also identified gaps in the literature where further research is warranted, particularly related to emerging trends of automation and digitalisation in the maritime industry.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".