Freeway Quality of Service: Perceptions from Tractor-Trailer Drivers
Bibliographic record
Abstract
Trucks make up a significant and growing portion of the traffic on freeways. The perceptions of tractor-trailer drivers regarding the quality of service on freeways are the subject of this research, with a focus on the factors that are important to this group of road users. Perceptions were determined using the standard qualitative inductive analysis approach through a focus group with professional tractor-trailer drivers. The results were compared with quality-of-service focus groups held for urban and rural freeway commuters. Freeway conditions in general were the most frequently mentioned factors and encompassed a variety of considerations. The three variables that together describe traffic conditions—travel time (or speed), traffic density (or maneuverability), and traffic flow—were all mentioned with regard to quality of service. Likely the most significant finding is that it is not traffic density that matters to these drivers; rather it is traffic flow. It appears that there is a comfortable operating range of highway speeds in which not much braking and acceleration-related gear changing are required. Other important themes included weather, attitudes toward other drivers, and road rage (i.e., aggressive driving). Participants also responded to questions about regional differences in quality of service. Safety was an issue that transcended or overlapped many other issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".