Implementing Red Light Camera Programs: Guidance from Economic Analysis of Safety Benefits
Bibliographic record
Abstract
Red light camera (RLC) systems are believed to decrease the right-angle crashes that they are targeting but to have the undesirable side effect of increasing rear-end crashes. This belief was confirmed in a before–after study of 132 RLC installations in seven U.S. jurisdictions, as reported in a companion paper. In that research, the extent to which the increase in rear-end crashes negates the benefits for right-angle crashes was unclear, given the perceptions of severity differences in the two crash types. This paper reports on an examination of the changes in crash costs, based on a consideration of rear-end and right-angle unit crash costs for various severity levels, to establish the aggregate effects of the RLC programs evaluated. Part of the project derived the required unit costs by using information from national U.S. databases. The overall results show a modest to moderate economic benefit of between $28,000 and $50,000 per treated site year, depending on assumptions made. The ability to aggregate economic costs across crash types and severity created the opportunity to try to isolate program implementation factors and intersection characteristics that would favor the installation of RLC systems by using the aggregate economic benefit at each RLC site as the outcome variable. This investigation found, for example, that the greatest economic benefits are associated with the highest total entering annual average daily traffic, the largest ratios of right-angle to rear-end crashes, and the presence of protected left-turn phases.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".