The effects of intersection collision warning systems on gap selection and stopping characteristics
Bibliographic record
Abstract
Over 8,500 fatalities occurred in the United States at intersections, or were intersection-related representing almost one-quarter of fatalities. Given the small percentage of roadway that intersections represent, the design of intersections provides a distinct challenge concerning safety, especially when poor sight vision is present. There has been a correlation found between smaller gap acceptance and crashes at intersections. Warning systems have been found to be an effective way to stop vehicles at intersections and identify acceptable gaps.\nThe Minnesota Department of Transportation installed an intersection collision warning system at select two-way stop-controlled intersections throughout the state in spring of 2015. The following study looks at changes in driving behavior resulting from the installation of the ICWS at the installation sites and nearby intersections that display similar traits. The metrics studied include the rate at which vehicles stop at the intersection, the location of stopping, and the gap acceptance.\nThe findings support the claim that cameras are effective in stopping vehicles at the intersections of installation. The stopping rate study saw an increase of 4.88% to 5.26% at treatment locations. The findings of the stopping location and gap selection studies were generally inconclusive. No treatment site displayed a significant increase in gap rejection rate.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".