Evaluation of the Red Light Camera Enforcement Pilot Project
Bibliographic record
Abstract
This paper describes how a two-year pilot study evaluating the combined use of two red light running treatments, red light cameras and stepped-up police enforcement, has recently been completed in the province of Ontario. Collision and volume data representing forty-eight different study sites distributed among six participating municipalities was analyzed for a five-year before period (1995 - 1999) and a two-year after period (November 20, 2000 - November 19, 2002) using the Empirical Bayes method. The estimated number of collisions occurring at the study sites was compared to the actual observed number of collisions that occurred to determine the safety effectiveness of the two treatments. The results indicate that fatal and injury collisions decreased 6.8 percent while property damage only collisions increased 18.5 percent at the study sites. These results show that the treatments have had an encouraging safety results as they have reduced the number of severe collisions from occurring at the study sites. A decrease in the number of angle collisions was also found, however this was offset by an increase in rear-end collisions. The increase in rear end collisions is similar to findings in other red light camera studies.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".