Freeway Users' Perceptions of Quality of Service: Comparison of Three Groups
Bibliographic record
Abstract
Focus group sessions were held with rural freeway commuters who use Highway 403 from Brantford to Hamilton, Canada, to identify the characteristics of their trip that helped determine their view of the quality of service for the trip. The results from that analysis were then compared with two other types of freeway users who had previously been studied: urban freeway commuters who use Queen Elizabeth Way from Toronto to Hamilton and tractor-trailer drivers. The findings suggest that despite some commonality among the three groups, each group valued a trip characteristic that they did not experience or at least could not be sure of. That characteristic differs across the three groups. Urban commuters were concerned about travel time, rural commuters about maneuverability, and truck drivers about steady traffic flow and physical road conditions. Despite these differences, an argument can be made that density, the service measure for freeways, is a reasonable proxy for most of these concerns. The results also suggest that it is harder to defend by using the same breakpoints on density for different types of freeways or even for different types of drivers. Although operational issues were the primary concern, it may also be appropriate to consider a second rating that addresses the quality of the facility as well as the existing level-of-service method that addresses the quality of operations on it.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".