Upstream Signalized Crossover Intersection: An Unconventional Intersection Scheme
Bibliographic record
Abstract
The impact of left turns on operation is probably the most significant factor in the performance of conventional intersections. As a result engineers have looked to alternative measures for dealing with left turns at intersections to improve performance, some of which have been unconventional schemes. The purpose of this paper is to discuss an unconventional intersection scheme, the upstream signalized crossover (USC), which is a four-legged intersection designed to eliminate left turn opposing conflicts by crossing the left and through traffic to the left side of the road at all four approaches prior to the intersection. The crisscrossing of traffic upstream of the intersection results in four additional secondary signalized intersections. VISSIM was used to model and analyze the unconventional USC intersection as well as a conventional intersection for comparison. The analysis revealed that the USC intersection can handle higher traffic volumes at reduced overall delays. In terms of left turn delay, the conventional intersection performed better at lower volumes. However, the USC was able to handle much higher left turn volumes while maintaining acceptable level of delay. In terms of through movement delay, the USC intersection was found to perform significantly better than the conventional intersection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".