Effective Speed Management Measures: Methodology and Application in City of Edmonton, Canada
Bibliographic record
Abstract
Every year, staff and members of Council of communities of all sizes along Canada receive numerous complaints from residents regarding vehicles speeding along residential roads. Although traffic volumes on these types of streets can be considered minor, traffic speeds detected in such types of road are causing a concern. Aside of law enforcement, the most common response to these complains is the implementation of traffic calming since this type of engineering measure has the potential not only to lessen the direct negative impact of road traffic, but to promote the integration of urban environments in which all modes of transportation can be adequately integrated as part of the roadway network. However, many of these attempts have shown questionable effectiveness in reducing travelling speed “corridor-wide” with a perceived effect limited to the area surrounding a traffic calming device. Through a comprehensive literature search and jurisdiction scan approach; this paper investigates the effectiveness of speed management measures while considering the following factors: 1) self-enforcement measures; 2) corridor-wide effect; 3) before and after study findings; and 4) potential impacts on transit routes, emergency vehicle response time, snow removal activities and pedestrian/cyclists. The results of this investigation are summarized in a selection matrix format for the most effective speed management measures. A methodology for applying these measures on collector roads is introduced by considering the context-sensitive characteristics of the study area and the speed management selection matrix. This methodology is then applied to two case studies on the City of Edmonton roads.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".