Mining Port Congestion Indicators from Big AIS Data
Bibliographic record
Abstract
In this paper, we introduce three maritime Port Congestion Indicators (PCIs) mined using Automatic Identification System (AIS) static and dynamic messages. The proposed indicators are spatial complexity, spatial density, and time criticality. To calculate the PCIs, we proposed three Big AIS Data mining algorithms to find the geohash area for certain precision, the convex hull area, and the average vessels proximity within the Port Area of Interest (AOI) and in the Period of Interest (POI). The indicators are calculated for the year of 2015 for three ports (Halifax, Hong Kong, and Singapore). The proposed PCIs capture the spatial complexity, spatial density, and time of service criticality. These indicators can be used by port authorities and other maritime stakeholders to alert for congestion levels that can be correlated to weather, high demand, or a sudden collapse in capacity due to strike, sabotage, or other disruptive events. We clustered the indicators for each port into three colour-coded (Green, Yellow, and Red) clusters corresponding to low, medium and high congestion levels. The centroids of these clusters can be used to predict future congestion levels of the port under consideration. To the best of our knowledge in published literature, this work is the first to introduce the application of AIS Big Data analytics to evaluate maritime port congestion levels.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".