Development of a Program Framework for Mobile Photo Radar Enforcement
Bibliographic record
Abstract
Speeding has been shown to increase the frequency and severity of collisions. Mobile photo radar enforcement (MPRE) programs aim to discourage speeding in order to reduce speed limit violations, and ultimately the frequency and severity of collisions. However, because enforcement resources are typically in short supply compared to the number of roadway facilities and locations in an urban area that could benefit from enforcement, a systematic management process can improve the effectiveness of a MPRE program. While there has been extensive research on how enforcement sites are chosen and how the impacts of MPRE are evaluated, significantly less attention has been given to the design of an integrated deployment, scheduling, and evaluation process specifically for MPRE. This paper presents a MPRE program design process, conceived in the context of the City of Edmonton’s MPRE program. The purpose of developing this program design is to provide planners and engineers with a systematic and analysis-based procedure to design and deploy a MPRE program. Potential MPRE locations are identified through a priority-based site selection process guided by speed violation and collision data from the City of Edmonton. MPRE operators follow a set of flexible guidelines for deploying to sites on a weekly basis. A schedule for program performance evaluation is proposed. Once operationalized, the MPRE program is expected to improve speed compliance by undermining drivers’ ability to predict the location and timing of MPRE, to ultimately reduce collisions and improve city-wide traffic safety.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".