Identification of Intersections with Promise for Red Light Camera Safety Improvement
Bibliographic record
Abstract
Red light cameras can be used as an alternative tool to supplement police efforts in enforcement against red light running, which is a major contributing factor to vehicle collisions at signalized intersections. The decision to install red light cameras should ensure overall improvement in traffic safety at signalized intersections. The major problem is deciding where and when to install the cameras in order to achieve the safety benefit. Therefore, guidelines are necessary for identifying and priority-ranking those intersections that have promise as sites for potential safety improvement. The primary objective of this study is to develop a safety analysis tool for estimating the safety impact of the installation of red light cameras at signalized intersections. Moreover, this research study provides a tool for identifying and priority-ranking problem intersections with respect to red light running within the entire roadway network under the jurisdiction of a particular agency. The proposed approach uses the empirical Bayes method, collision prediction models, and collision modification factors to estimate the safety changes upon installation of a red light camera at a signalized intersection. The generalized estimating equations technique with the assumption of negative binomial error distribution was used for development of the collision prediction models.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".