Improving Transportation Safety: Calgary Safer Mobility Plan 2013-2017 Case Study
Bibliographic record
Abstract
On average, 94 collisions occur in Calgary each day. The City of Calgary Transportation Department is committed to continuously improving road safety for all transportation network users, and has developed a comprehensive transportation safety management system in support of municipal plans, Alberta's Traffic Safety Plan, Canada's Road Safety Strategy 2015, and in line with the Global Decade of Action as put forward by the United Nations and the World Health Organization. The Calgary Safer Mobility Plan (SMP) is intended to act as the first step towards a formal, Calgary-specific and evidence driven transportation safety management process. The SMP incorporates safety activities currently in place, builds upon them, and introduces new initiatives based on a number of guiding principles, including the following: Multi-modal Safer Systems approach; multi-disciplinary partnerships; evidence-based and data-driven focus on best practices; promoting the 5 Es (engineering, enforcement, education, evaluation and engagement). The SMP introduces strategies and programs to achieve proposed targets. Progress in achieving those targets will be tracked annually in a Safer Mobility Annual Report Card and Action Plan. The final evaluation will identify the most effective actions, helping to direct limited resources towards strategies and programs that are most successful in reducing fatalities and injuries. The SMP aims to achieve an overall reduction of 10 percent in fatality and injury collisions within five years, ultimately striving towards zero casualties on Calgary's transportation network. The targets are achievable with the help of an accountable, evidence-based, and innovative transportation safety management process. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".