Rural Freeway Level of Service Based on Traveler Perception
Bibliographic record
Abstract
The concept of level of service (LOS) is meant to reflect the trip quality a traveler will experience on a roadway or other transportation facility. The objective of this study is to provide insight into how road users perceive trip quality on rural freeways and to examine how the existing service measure (density) relates to the perceived trip quality. Study participants were shown a series of video clips of rural freeway travel from a driver's perspective. They then filled out survey forms indicating their opinion of the trip quality provided by the conditions in the video clip. The survey participants were also asked to give background information about themselves and their driving habits. The data from the surveys were analyzed on the basis of an ordered probit model. The first model used only density as a predictive factor. The second took other roadway and traffic characteristics into account, and the third examined all the significant factors obtained from the survey. The “density only” model confirmed that density is a strong indicator of travelers’ perceptions of trip quality. The other models showed the significance of other traffic and roadway factors in the perception of trip quality, as well as some socio-economic information and personal driving habits. A set of LOS thresholds was also calculated for the “density only” model on the basis of the survey participants’ responses. The resulting thresholds were considerably lower than the Highway Capacity Manual (HCM) thresholds for all LOS rankings. This suggests that travelers’ tolerance of congestion is lower on rural freeways than the HCM indicates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".