Disaggregate safety evaluation for signalized intersections and evaluation tool
Bibliographic record
Abstract
Abstract This study was to evaluate traffic safety of four‐legged signalized intersections and to develop a spreadsheet tool for identifying high‐risk intersections taking into consideration vehicle movements, left‐turn signal phase types, and times of day. The study used data from Virginia and employed count data models and the empirical Bayes (EB) method for safety evaluation of such intersections. It was found that crash pattern defined by vehicle movements involved in a crash and time of day are important factors for intersection crash analysis. Especially for a safety performance function (SPF), a model specification (Poisson or NB), inclusion of left‐turn signal types, type of traffic flow variables, variable functional forms, and/or magnitudes of coefficients turned out to be different across times of day and crash patterns. The spreadsheet application tool was developed incorporating the developed SPFs and the EB method. As long as Synchro files for signal plans and crash database are maintained, no additional field data collection efforts are required. Adjusting the developed SPFs and the spreadsheet for recent traffic and safety conditions can be done by applying the calibration methods employed in the SafetyAnalyst software and the Highway Safety Manual. Implementing the developed tool equipped with streamlining data entry would greatly improve accuracy and efficiency of safety evaluation of four‐legged signalized intersections in localities and highway agencies that cannot operate the SafetyAnalyst. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".