Capacity of Shared Left Turn Lanes—Comparative Analysis
Bibliographic record
Abstract
This paper compares the capacities of shared left turn lanes obtained by four methods for varying lane configurations, through and left turn volumes, and traffic signal timings. The 1997 Highway Capacity Manual (HCM) , Canadian, SIDRA, and Levinson methods were analyzed for left turn volumes ranging up to 250 vehicles per lane per hour (vph) for both 60- and 90-s cycles, assuming 50% effective green time per cycle. Scenarios were tested assuming both equal and unequal volume. More than 700 individual computations were performed. The methods provide generally consistent patterns for each scenario tested. The 1997 HCM method (unlike the 1994 HCM method) consistently provided higher capacities than the other three methods for single-lane approaches. The SIDRA method consistently provided the lowest capacities on multilane approaches where left turn volumes exceed 100–150 vph. The research suggests further field tests and analyses to see if modifications in left turn equivalency factors for single-approach lanes associated with the 1997 HCM method are desirable to bring the results more in line with those of the other models. The general consistency of the shared lane capacities obtained by the Canadian and Levinson methods, along with their simplicity, clarity, and ease of use make them well suited, especially for quick response applications.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".