Statistical analysis of fluvial trajectories based on AIS database for the construction of a bridge
Bibliographic record
Abstract
The French metropolis, Rouen Normandie, has a project of a new bridge requiring temporary pier in the river Seine during the building phase. The location of this pier in the river is constrained by mechanical reasons related to the construction of the bridge. The purpose of this study is to find a position of this pier in the mechanically constrained area which minimizes vessel traffic obstruction and therefore collision risk. This type of study is classically carried out by using complex and multiple vessel dynamics simulation software in order to assess the risk for a given vessel to crash into the pier. The novelty of this study is to propose a statistical study based on the database of Automatic Identification System (AIS) of the Vessel Traffic Service (VTS) that is an automatic tracking system relying on ships transceivers. The technical objective is to identify low risk areas according to vessel speeds and sizes. Both factors should have an impact on the maneuverability of ships to avoid pier collision risk. Among all trajectories, straight line trajectories are selected based on statistical methods. The computation of prediction intervals of these trajectories delimits navigation zone. If all the straight trajectories are taken into account, the navigation zone extends to the whole river surface, which is not an helping result. However, by focusing on large and high speed vessels trajectories the navigation zone of these weak maneuverable ships is more centered in the middle of the river and represents only 25% of the river area. The complement of this area delineates possible locations of the temporary pier of the bridge in the river that do not disrupt vessel traffic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".