Système d'aide à la régulation et à la reconfiguration des réseaux de transports SVM et algorithme à colonie de fourmis
Bibliographic record
Abstract
The public transport networks regulation is a real-time task and it becomes more and more difficult for a human operator, with a number of stations, vehicles and modes that does not stop increasing. Sometimes there are very complicated cases of disturbances and the regulator must propose a new planning of the network with a spatial reconfiguration and an hourly regulation. It is a task even more delicate than the regulation. This article proposes a regulation and reconfiguration decision support system. We use a first classification algorithm SVM (Support Vectors Machines) for the regulation and the second ant colony algorithm for the spatial and hourly reconfiguration. In this approach, we used a new idea of dynamic local search. The obtained results confirm the good performance of both approaches for the adaptation of a multimodal transport network to the real exploitation conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".