Assessing the Effectiveness of Automated Speed Enforcement in Durham Region, Ontario, Canada
Bibliographic record
Abstract
Speeding remains a leading cause of traffic accidents, contributing to nearly a third of all deaths. To mitigate this risk, speed enforcement measures are widely used. Automated Speed Enforcement (ASE) programs, in particular, have been globally evaluated for their efficacy in promoting road safety. This article presents the first study to evaluate the impact of ASE in different ASE periods and spatial locations in the regional municipality of Durham, Ontario, Canada. Our research methodology comprises two main components: temporal and spatial analysis. The Temporal analysis involves comparing traffic data across three ASE periods: the warning period, active ASE period, and post-ASE camera removal period, across multiple rotations. Spatial analysis examines traffic data at three spatial positions: upstream (before), at, and downstream (after) the ASE camera. Speed radars were deployed at two ASE sites in the Durham Region, ON, Canada, to evaluate the temporal and spatial effectiveness of ASE. Results indicate a significant reduction in speed violations at both sites across multiple rotations. Moreover, the study identifies a long-term positive impact of ASE on driver behavior at the study sites.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".