High occupancy vehicle lanes — worldwide lessons for European practitioners
Bibliographic record
Abstract
Europe has long provided bus lanes and on-street bus priority measures.High Occupancy Vehicle (HOV) programs expand that practice to include private shared-ride vehicles (carpools) and other priority vehicles.There are a few HOV lanes in operation in Europe, and interest is growing in their potential applicability in congested urban roadways.With over 200 HOV lane projects now in use on streets and highways around the world, there are useful lessons to be learned by those considering the HOV option in the European context.The reasons for project successes and failures are outlined, with particular attention paid to the constraints and operational issues prevalent in the European environment.Critical issues such as enforcement, conversion from general purpose use, design, and underutilization are explored.The documented effectiveness of HOV facilities in influencing mode choice is summarized.Finally, the future of HOV priority within the urban transport system is discussed, touching on high-tech enforcement solutions, HOV priority within tolled facilities, and the integration of HOV initiatives within broader Transportation Demand Management programmes.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".