Implementation of a road attribute changes data-exchange platform and a transferability assessment: The case of TN-ITS Cyprus
Bibliographic record
Abstract
Following the EU Directive for the EU-wide real-time traffic information services (2015/962) as well as the increasing demand for accurate and high detail maps; map providers and road authorities need to work closely to be able to provide drivers with the most accurate and up-to-date information regarding the road network infrastructure. Drivers access information regarding the road network infrastructure through navigators which contain maps from commercial map providers. However, map providers collect information for the road network after field surveys which take place every few months or years; at the same time, the road network is constantly changing by road authorities following regulation changes. The TN-ITS project aim is to provide the necessary support to create data exchange platforms regarding changes in the road network between road authorities and commercial map providers. This project is expected to close a gap in the information flow between the two parties as well as to increase road safety. This paper describes the implementation of the TN-ITS service in Cyprus, covering the highway and intraurban road network followed by a transferability analysis of the developed platform to assess the possibility of using the same platform to cover a larger area of the road network.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".