Effect of Red-Light Cameras on Capacity of Signalized Intersections
Bibliographic record
Abstract
Red light running (RLR) is one of the most common violations drivers commit at signalized intersections. To avoid RLR violations, some drivers may decide to stop abruptly, even though they had the opportunity to cross the stop line before the onset of the red light. This action happens more frequently at intersections with a red-light camera (RLC). The consequence of this change in drivers’ stopping behavior is the potential reduction of the usable clearance interval and the slight decline in the intersection capacity. However, different agencies’ guidelines take different approaches to estimate the clearance lost time (CLT) for capacity analysis of signalized intersections; there is not an adjustment factor for considering the impact of RLCs. In an attempt to quantify the effect of RLCs on the capacity of signalized intersections, field data were collected at eight intersections: four with RLCs and four without, in the cities of Opelika and Auburn, Alabama. A total of 1,191 cycles and a total of 1,863 drivers’ responses to clearance intervals were used to estimate the CLT. It was found that the estimated CLT at the approach with a RLC is approximately 2.7 s longer than the default value presented by one set of guidelines and about 1.1 s longer than those in another. On average, the unused yellow time was a half-second longer in RLC intersections than the intersections without RLCs.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".