Investigating Time Halo Effects of Mobile Photo Enforcement on Urban Roads
Bibliographic record
Abstract
This paper investigates the time halo effects of mobile photo enforcement (MPE) on urban arterial and collector roads. Speed data were continuously recorded at nine locations for five consecutive weeks during the summer months of 2015 in Edmonton, Alberta, Canada. Each location was enforced according to a predefined deployment plan related to the regular working hours of MPE personnel. A time series intervention analysis that used percentages of speed limit violations was conducted to determine and to understand the time halo effects of MPE. The results of the analysis indicate that time halo effects existed at all study locations and that significant reductions in speed limit violations occurred because of MPE. The authors concluded that, on average, if an MPE unit was deployed eight times during a week for 22 h (approximately 2.7 h per visit) at an urban location, it would produce a time halo effect that would extend for approximately 5 days and reduce speed limit violation rates by almost 19%. In addition, the number of enforcement visits per week, the number of enforcement hours per week, and the average hours per visit were found to be strongly correlated to the longevity of the time halo effects and the reduction in speed limit violations. The findings of this study can be used to significantly increase the coverage of MPE programs and the safety benefits associated with MPE operations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".