Investigation of the performance of two-way left-turn lane on roads with staggered intersections
Bibliographic record
Abstract
A simulation based on VISSIM and its external driver model dynamic link library (DLL) was used to investigate the efficiency and safety of a two-way left-turn lane (TWLTL) on roads with staggered intersections (SIs). The impacts of traffic volume, ratio of left-turn vehicles, and stagger distance on SIs and conventional cross intersection (CI) were simulated with the following results: (1) SIs with stagger distance shorter than 200 m show few advantages in terms of average delay over a CI irrespective of traffic volume and left-turn ratio; (2) In contrast, SIs with stagger distance longer than 200 m show advantages that, however, disappear with an increase in traffic volume and left-turn ratio; and (3) SIs show a significantly higher number of traffic conflicts than CIs, indicating that they have more serious safety problems. These results should help traffic researchers and practitioners decide whether it is feasible to establish SIs on existing roadways with TWLTLs and the appropriate stagger distance for SIs, as well as whether it is appropriate to change a road that has dense SIs into a TWLTL cross section.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".