Multijurisdictional Safety Evaluation of Red Light Cameras
Bibliographic record
Abstract
The use of red light camera (RLC) systems has risen dramatically in the United States in recent years. The size of the problem, the promise shown by RLC systems in other countries, and the paucity of definitive U.S. studies have motivated a multijurisdictional U.S. study. The fundamental objective of this study, which was sponsored by FHWA, was to determine the effectiveness of the RLC systems in reducing crashes at monitored intersections as well as jurisdictionwide. Phase I involved the development of a detailed experimental design that included collection of background information, establishment of study goals, selection of potential study jurisdictions, and specification of statistical methodology. In Phase 2, an empirical Bayes before-and-after study used data from seven jurisdictions across the United States, with a total of 132 treatment sites. Effects detected were consistent in direction with those found in many previous studies—a decrease in right-angle crashes and an increase in rear-end crashes—although both effects are somewhat lower than those reported in many sources. The extent to which the increase in rear-end crashes negates the benefits for right-angle crashes is unclear and points to the need for an examination of the economic cost of crashes, which is the subject of a companion paper, to aggregate the effects on rear-end, right-angle, and other crash costs. That second paper seeks to isolate all factors that would favor the installation of RLC systems by using the aggregate economic benefit as the outcome variable. There were weak indications of a spillover effect, which point to a need for a more definitive, perhaps prospective, study of this issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".