Upstream Signalized Crossover Intersection: Optimization and Performance Issues
Bibliographic record
Abstract
Left turns have a significant impact on the operation and performance of signalized intersections. Therefore, researchers have investigated alternative measures to improve the performance of intersections with heavy left-turn movements, some of which have been unconventional schemes. One of these schemes is the upstream signalized crossover (USC), 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. Crisscrossing action is achieved through the use of a secondary two-phase signal at each crossover point. This research investigated signal optimization strategies for USC intersections and identified important operation issues. The VISSIM microsimulation program was used to model and analyze the unconventional USC intersection and, for comparison, a conventional intersection. The analysis revealed that, for relatively balanced volumes, a USC intersection can significantly reduce average vehicle delays, particularly when the volumes entering the intersection are relatively high. More important, the capacity of the USC intersection was found to be approximately 50% greater than that of a conventional intersection with similar geometry under balanced traffic volumes. For highly unbalanced volumes, particularly when the intersection volumes were relatively low, a conventional intersection outperformed the USC intersection. Overall, the USC intersection showed considerable potential for situations in which one or more of the following conditions exist: (a) intersection volumes are balanced and near or over the capacity of a conventional intersection, (b) traffic volumes are somewhat unbalanced, but the overall entering volumes are too high to be accommodated with a conventional intersection, or (c) the intersection has heavy left-turn volumes that cause excessive delays.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".