Optimizing the built environment for pedestrian safety in an ageing society: Toward an inclusive approach
Bibliographic record
Abstract
With the longstanding overrepresentation of seniors in pedestrian injuries and fatalities, added to the prevalence of North-American ageing population, there is a sense of urgency for public health and transport planners to better understand the conditions surrounding senior injury risk. However, planners and practitioners are still challenged by a North-American built environment often optimized for vehicles rather than active and ageing road users. Researchers, on the other hand, still do not fully understand the nuances between younger and older injury factors, and particularly the effect of senior pedestrian exposure on their injury risk. \nThe primary research question in this thesis seeks to determine if senior injury factors at signalized intersections differ from those of the younger and to what extent? In a secondary fashion, this thesis explores the definition of an optimal built environment for pedestrian safety in an ageing society. In addition, it explores determining whether or not subpopulation models are more inclusive. Chapter 4 presents an injury regression analysis comparing 479 younger and 107 older pedestrian injuries that occurred at 191 signalized intersections in Montreal, a study deemed “practice ready” that was presented at the 2015 Transportation Research Board (TRB) annual meeting. \nAmong other results, the center median refuge was found to reduce injury probability in seniors by 70%, implying that it has the potential to be a strong countermeasure in an ageing society. Results of this research revealed nuances between younger and older injury factors, and suggest that the optimal built environment for pedestrian safety of an older society may indeed differ from one optimized for the younger. In addition to being more inclusive, findings suggest that without subpopulation models, safety or health performance indicators of senior pedestrians may be overlooked.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.009 |
| 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".