Effect of Grade on Operating Speed and Capacity of Two-Lane Rural Roads
Bibliographic record
Abstract
Two-lane rural roads constitute a significant part of the roadway system in India. Geometric characteristics such as facility type, lane width, shoulder width, and horizontal and vertical alignments, are essential parameters that influence vehicle behavior and traffic flow characteristics on two-lane rural roads. Among all geometric characteristics, the magnitude of the grade most substantially impacts the operational characteristics of traffic flow on two-lane rural roads. The present study investigates the effect of grade on the operating speed and capacity of two-lane rural roads under mixed traffic conditions. Traffic video data for eight road sections with grades varying from 1% to 8% were collected under dry weather conditions. The investigation revealed a significant effect of grade on the operating speeds of different vehicle types. The operating speed decreased with an increase in the grade magnitude. The road capacity was derived by calibrating various single regime models. The Northwestern model was deemed appropriate to derive the capacity values, based on theoretical and statistical investigation. The results showed that the capacity decreases by 6.4% with every 1% increase in grade. Further, the effect of grade on passenger car units (PCU) of different vehicle types was investigated. For varying volume-to-capacity (V/C) ratios, it was observed that the PCU of heavy vehicles increases as the magnitude of the grade increases. The present study develops operating speed and capacity prediction models as an essential practical outcome. The developed models can facilitate planners and traffic engineers to estimate operating speed and capacity based on the magnitude of the grade.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".