Evaluation of Intersection Safety Camera Program in Edmonton, Canada
Bibliographic record
Abstract
Enforcing traffic signal compliance in urban areas can be a difficult task, as the process is often limited by police resources and by traditional enforcement methods. Therefore, conventional traffic enforcement should be supplemented with advanced technologies, such as intersection safety cameras. Intersection safety cameras, often referred to as red light cameras (RLCs), are being used increasingly to help communities enforce against deliberate red light running. Since the 1970s, Europe, Australia, and North America have been using photo enforcement technology to improve safety at intersections. The City of Edmonton, Canada, has used automated photo enforcement as part of the overall traffic enforcement activity. The Intersection Safety Camera Program started as a pilot project in 1998, which quickly expanded into a comprehensive program for the entire city. Because a significant period had elapsed since the inception of the program, the Edmonton Police Commission wanted a study to determine the overall safety effect of the photo enforcement program. This paper describes the evaluation of Edmonton's Intersection Safety Camera (ISC) program. The paper describes the methodology used in the evaluation, the data collection–compilation effort required for the evaluation, and, finally, the results of the program measured in relation to reducing the number of collisions at the intersections that were treated with photo enforcement cameras. Also included is a summary of the literature associated with the deployment of RLCs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".