Comparison of Driver Yielding for Three Rapid-Flashing Patterns Used With Pedestrian Crossing Signs
Bibliographic record
Abstract
Flashing traffic control devices can help draw drivers’ attention to the traffic control device and to the area around the device. An example of a device that has resulted in significant improvements in increasing driver yielding to crossing pedestrians is the rectangular rapidflashing beacon (RRFB). Studies have been conducted in several locations, including Florida, Texas, Oregon, Michigan, Arizona, Wisconsin, and Calgary, AB. (See references 1 through 10.) These studies show a large range in the number of driver yielding responses for the RRFBs, extending from a low of 22 percent to a high of 98 percent. This wide range in yielding indicates that device, site, or roadway characteristics are potentially affecting the driver’s decision to yield. Even with this wide range, the use of RRFBs has resulted in more drivers yielding to crossing pedestrians. Although the RRFB is allowed under interim approval from the Federal Highway Administration (FHWA), there is 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 (NCUTCD), 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. The initial research studies did not address certain issues that the STC believes are important in crafting language suitable for the MUTCD. For example, will other flash patterns be just as effective as the initial flash pattern that was evaluated and approved by FHWA? This TechBrief describes the methodology and results from an open-road study sponsored by FHWA that examined driver yielding behavior at crosswalks with three different flash patterns used with yellow, rapid-flashing beacons.\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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".