Arterial Road Speed Reduction Program - Springfield Road, Kelowna, BC
Bibliographic record
Abstract
Springfield Road in Kelowna, BC by 2011 had become one of the community’s highest collision frequency corridors over the past decade. Analysis of the types of collisions occurring at the corridor intersections indicated an over-representation of collision types associated with excessive speed. Speed studies were conducted that identified an 85 percentile speed of 71 km/h on this corridor that is posted at 50 km/h. These results reflected the growing number of complaints from area residents regarding unsafe speeds. Springfield Road is a four-lane arterial roadway with bike lanes and sidewalks on both sides. It is a key commuter route into the City Centre, but also serves local access as residential lands have access onto the roadway. The multi-function needs of the corridor increases the importance of compliance to appropriate posted speeds. It was understood that a concerted effort utilizing a ‘5E’ approach – Engineering, Encouragement, Education, Enforcement and Evaluation – was needed to reduce speeds. A working group of City staff, RCMP, and the Insurance Corporation of British Columbia (ICBC) was struck to develop an Integrated Speed Reduction Plan. The program results highlight how driving behaviour be influenced by coordinating efforts of partnering agencies and integrating speed reduction strategies. All measures are highly visible and have reduced the frequency of complaints from area residents. Implementation of the 5E measures has resulted in a 7% reduction of the 85th percentile speeds and 8% reduction in 50th percentile speeds. The speed reductions have been sustained over the past three years. Future evaluation will include the continuation of corridor speed monitoring. In addition, an evaluation of the impact of the measures on addressing collisions is currently being undertaken by the University of British Columbia – Okanagan and should be available by year end.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".