Comparison of Driver Yielding for Rectangular Rapid-Flashing Beacons Used Above and Below Pedestrian Crossing Signs
Bibliographic record
Abstract
Traffic control devices, such as rectangular rapid-flashing beacons (RRFBs), have been shown to increase the number of drivers yielding to crossing pedestrians. Evaluations of field installations of these devices have been conducted in several locations, including Florida, Texas, Oregon, Michigan, Arizona, Wisconsin, and Calgary, AB. (See references 1 through 10.) Before-after studies have shown a large increase in driver yielding between the before period (range of 1 to 83 percent) to the after period (range of 38 to 98 percent). Although the RRFB is allowed under interim approval from the Federal Highway Administration (FHWA), there is a growing interest in adding it to the Manual on Uniform Traffic Control Devices (MUTCD).(11,12) The Signals Technical Committee (STC) of the National Committee on Uniform Traffic Control Devices, which assists in developing language for chapter 4 of the MUTCD, is interested in research and/or assistance in developing materials on the design, application, and effectiveness of the RRFB. One of the areas for which the STC is seeking advice is the position of the beacons relative to the crossing sign. For example, does positioning the beacons above the sign improve the driver’s ability to see a pedestrian crossing or waiting to cross and thus yield to crossing pedestrians?\nThis TechBrief describes the methodology and results from an open-road study sponsored by FHWA that examined driver yielding behavior at crosswalks with RRFBs positioned above the pedestrian crossing sign and at crosswalks with RRFBs positioned below the sign.\n
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".