Evaluation of Speed Management Strategies: A Before and After Study
Bibliographic record
Abstract
In recent years there has been a considerable amount of interest on the part of road authorities and their residents in stopping the progressive erosion of residential quality of life caused by street traffic. High traffic volumes and speeds, especially on local streets, potentially impact residents in terms of safety, noise, and pollution. Road authorities have expressed interest in implementation of speed management strategies (as a means of decreasing the cars’ dominance) that are relatively low-cost in nature and easily implemented in the short-term periods (within 1-5 years). Speed management strategies aim at reducing the frequency of vehicle with speed too fast for the prevailing conditions and increase compliance with the posted speed limit. These speed management strategies encompass a wide range of measures that include enforcement, education, and engineering. It is critically important for road authorities to understand the effectiveness of these measures on operating speed of vehicles. This paper involves evaluation of three speed management engineering measures: (1) installation of the speed limit signs, (2) peripheral traverse bars, and (3) gateways where speed limit zones and land-use change. An observational before and after study with comparison group is used to evaluate the effects of these measures on speed. The data used for this study were collected from Trafalgar Road in the Regional Municipality of Halton, Ontario where these speed management measures were implemented. The results of the before and after study suggested that implementation of the speed management strategies resulted in the reduction of the average operating speeds, in the range of 4 to 8 percent; however these results were not statistically significant at a 95% confidence interval. The results of this study will assist road authorities in the decision making process for potential implementation of these measure in their jurisdiction for speed management purposes.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".