Leading Through Intervals versus Leading Pedestrian Intervals: More Protection with Less Capacity Impact
Bibliographic record
Abstract
When pedestrian, bike crossings, or both are concurrent with a vehicular phase, leading through intervals (LTI) and leading pedestrian intervals (LPI) are signalization techniques that provide a partially protected crossing. With LPI, for a short interval at the start of the crossing phase all traffic is held, enabling some pedestrians to arrive at the conflict zone and thus reinforce their priority before turning vehicles are released. LTI functions similarly except that during the leading interval only turning traffic is held; through traffic is allowed to run. This lessens the negative effect on capacity of LPI, and consequently allows LTI to have a longer leading interval, thus affording pedestrians and cyclists greater protection. Experience of LTI in the cities of Montreal, New York, and Charlotte is reviewed. A model is developed to estimate capacity loss from using LPI and LTI for a range of scenarios in which right turns share a lane with through traffic, in which case LTI can indirectly block through vehicles positioned behind a turning vehicle. While LTI’s capacity loss increases with the proportion of right turns, for the wide range of right turn proportions tested, it is still far lower than the capacity loss for an LPI of the same length, especially on multilane approaches.
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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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".