IMPROVING THE SAFETY OF OLDER PEDESTRIANS: FROM UNDERSTANDING OF THE PROBLEM TO GENERATING STRATEGIES
Bibliographic record
Abstract
This paper presents information designed to help better understand the current situation regarding the safety of older pedestrians in Winnipeg. This is done through a comprehensive analysis of pedestrian collisions and field investigations at selected locations in the city. A series of strategies is generated to improve the safety of pedestrians in general, but particularly that of older pedestrians, based on the findings from the research. This paper involves five elements: (1) a description of the creation of the database used for analysis; (2) a comprehensive analysis of collisions involving pedestrians in Winnipeg over the last 12 years; (3) a detailed analysis, using geographic information systems, about the geographical characteristics of pedestrian collisions; (4) field investigations at selected locations; and (5) generation of potential strategies to improve the safety of older pedestrians. The research shows that there are different types of challenges faced by road safety professionals to improve the safety of older pedestrians in Winnipeg. Many opportunities exist through different strategies involving road engineering and the application of technology. Some of these opportunities are also identified in this paper.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".