Safety Evaluation of Red Light Camera and Intersection Speed Camera Programs in Alberta
Bibliographic record
Abstract
Intersection related crashes constitute more than 20% of fatal crashes in the US. Red light running and high speed have been reported as contributing factors for severe crashes at signalized intersections that can be mitigated by using a system of automated enforcement. Red light cameras (RLC) have been deployed by many jurisdictions in the world. Unlike the RLCs, intersection speed cameras (ISC) have not commonly been used. RLCs have been evaluated by a number of researches; however, few studies quantified spillover effects in evaluating the effectiveness of RLCs. Additionally, little research has been conducted in evaluating the effectiveness of ISCs. In this study, data from various jurisdictions in the Province of Alberta in Canada were used to evaluate the effectiveness of both RLCs and ISCs with special emphasis on spillover effects for RLCs. Observational before and after studies were conducted using the empirical Bayes method to evaluate the changes in the intersections safety performance as a result of individual deployment of both RLCs and ISCs. Spillover effects for RLC intersections during the before period (before RLC activations) by considering a control group of unsignalized intersections were taken into account for before and after evaluation of RLCs. In addition, the magnitudes of spillover effects of the RLCs on all non-RLC signalized intersections (including RLCs during the before period) were also quantified. It was found that severe crashes decreased and property damage only crashes slightly increased for both RLCs and ISCs. Also, angle crashes significantly decreased (37.7% for RLCs and 31.3% for ISCs) and rear-end crashes increased (7.7% for RLCs and 9.4% for ISCs). In terms of spillover effect on all nonRLC signalized intersections, a significant drop in crashes (10.7% for Total, 6.6% for PDO, 22.7% Severe, 2.4% Angle, and 14.6% rear-end crashes) was noted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".