General deterrence effects of red-light camera and warning signs in traffic signal compliance in British Columbia
Bibliographic record
Abstract
Objective: This study investigated the general deterrence effects of red light camera and the warning signs on traffic signal compliance in British Columbia. Methods: The study was conducted in two major cities in the interior region of the province, using a two-staged, quasi-experimental, treatment-control group design. In the first stage, the BC intersection safety camera program (ISC) was implemented in the two study cities simultaneously. In the second stage, 42 extra warning signs, informing the drivers of the ISC program were erected in and around the signage-treated city. In total, close to four million of vehicles were observed over the study periods, using an automated machine vision data collection technique. The data were subjected to Negative Binomial regression analysis, recognizing over-dispersion in violations over Poisson models. Results: The analysis revealed a 69% reduction in red-light violation rate at the study intersections one-month after the introduction of the program. After 6-month program operation, the reduction rate declined to 38%. The study did not found substantial and significant incremental effect of warning signs on red-light violation over and above the standard program configuration. Conclusion: The study found a general deterrence effect of ISC on driver traffic signal compliance. The study did not find evidence for signage effect. The result of the study seems to support the current BC program model, against a potentially cost-saving alternative of using extensive warning signs in lieu of the more expensive camera units.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".