Assessment of level of service measures for two-lane intercity highways under heterogeneous traffic conditions
Bibliographic record
Abstract
Many researchers have studied the performance of two-lane intercity highways with the help of different measures. In the current study, the performance of such highways under heterogeneous traffic conditions was examined by using several speed and followers related measures. The data were collected from five study sites located in different regions of India. A new methodology was proposed where followers were identified by using a speed difference (between two consecutive vehicles) range of −4 to + 10 km/h and gap threshold value (lower than a particular gap value) of 10 s. By using acceptance curve method, different critical gap values were suggested for each site to identify the followers. Out of all the performance measures, the number of followers as a proportion of capacity (NFPC) and follower density were found to be the best and second best parameters. Finally, different level of service ranges were proposed based on NFPC using cluster analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".