SMART SPEED - RESULTS FROM THE LARGE SCALE FIELD TRIAL ON INTELLIGENT SPEED ADAPTATION IN UME, SWEDEN
Bibliographic record
Abstract
The Swedish National Road Administration (SNRA) was in 1998 assigned the task to carry out a national programme on large scale field trials on Intelligent Speed Adaptation (ISA). The task followed from the very successful smaller trials in the cities of Ume and Eslv carried out 1996-97. The planning for the field trials was carried out in 1998, identifying the cities of Ume, Lund, Borlnge and Eslv as candidates for the trials. A budget totalling ca. 8 M US$ / 9 M was allocated for the period, with approximately 1/3 for the Ume field trial - Smart Speed. The Ume trial is using a beacon-based solution together with in-vehicle intelligence. In short, in the Smart Speed field trial the vehicles are equipped with an in-vehicle device the size of a cigarette box which gives a light (flashing red) and noise (increasing beep) if the driver exceeds the speed limit within the field trial area including 2/3 of the city area. The background to and design of the field trial and its organisation was presented at the 6th ITS World Congress in Toronto 1999. This paper describes the success of the project two years later when the system is in full operation and substantial user investigations have been carried out.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".