Application of Innovative Time Series Methodology to Relationship between Retroreflectivity of Pavement Markings and Crashes
Bibliographic record
Abstract
The innovative time series methodology that is presented was used to examine the relationship between the safety impact of longitudinal pavement markings and their retroreflectivity. The need for the methodology was determined through an examination of the pavement marking safety literature, the desire to build on previous retroreflectivity research, and an understanding of how retroreflectivity is a function of the age of pavement markings and markers. The time series methodology involves solving for multipliers that represent the change in the expected number of crashes as a function of pavement marking retroreflectivity while simultaneously solving for seasonal effect multipliers. The time series methodology is explained in detail; its usefulness has been demonstrated through the analysis of 8 years of pavement marking and marker installation data, traffic volumes, and crash data. Safety effect multipliers were solved for yellow and white pavement markings separately and in combination. The multipliers covered different road types and crash severities by using retroreflectivity models and California's data for more than 118,000 nonintersection, nondaylight (night, dawn, and dusk) recorded crashes. To apply the time series methodology, it was necessary to develop retroreflectivity models as a function of age, color, marking material type or marker type, climate region, and amount of snow removal. The results showed that retroreflectivity levels have no effect to a small effect on the safety performance of those roadways maintained to a reasonable level of pavement marking.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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".