Low Cost Operational Improvements at Freeway Exit and Entry Ramps
Bibliographic record
Abstract
A pilot study was conducted by The Ministry of Transportation, Ontario with the goal of providing drivers with enhanced notification and guidance for freeway must exit lanes and discouraging the use of on and off ramps for queue jumping. The use of enhanced pavement markings, pavement marking arrows and additional signing gives drivers advanced notice in regard to which lane(s) exit the freeway, allowing drivers additional time to change lanes accordingly. The enhanced pavement markings and signing at entrance and exit ramps also discourage drivers from using on ramps and off ramps to by-pass congestion on the freeway. A solid line was painted to the right of the existing dashed line at must exit lanes, and to the left of the existing dashed line at entrance ramps. Pavement marking arrows were painted on the asphalt approximately 450 and 600 metres from the freeway exit, and additional ground mounted lane designation signs were installed. Before and after studies were conducted at six locations. The studies concluded that the enhanced pavement markings, pavement arrows and additional signing at exit ramps were very successful in reducing queue jumping and last minute lane changing. The studies conducted at the entrance ramps were inconclusive. (A) For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".