Developing Level-of-Service Criteria for Two-Lane Rural Roads with Grades under Mixed Traffic Conditions
Bibliographic record
Abstract
Traffic operations on two-lane rural roads differ substantially from those on divided carriageways due to vehicular interactions between traffic flows in the opposite directions. With the presence of grades and mixed (heterogeneous) traffic, traffic operations on two-lane highways become even more complex and challenging. The present study developed level-of-service (LOS) criteria for assessing the performance of two-lane rural roads with grades. Eight two-lane undivided study sections with grades varying from 1% to 8% were selected. The suitability of well-established performance measures such as percent time spent following (PTSF), number of followers per capacity (NFPC), follower density (FD), average travel speed (ATS), and percent of free-flow speed (PFFS) was evaluated. The results showed that the foregoing performance measures were not practically applicable for characterizing the operational LOS for two-lane rural roads with grades. A new performance measure termed density ratio (DR) was developed in the present study. The ATS, PFFS, and FD measures for different grades revealed no significant difference when visualized at similar DR ranges. Therefore, DR can be considered an effective measure for developing LOS criteria for such roads. The criteria were first developed using DR, ATS, and FD; subsequently, a design LOS was derived.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".