A Built Environment for an Ageing Society: A Subpopulation Analysis of Pedestrian Crashes at Signalized Intersections in Montreal, Canada
Bibliographic record
Abstract
Concern for pedestrian safety has grown recently because of ageing population not only in North America but globally. Meanwhile the overrepresentation of older adults in fatal pedestrian crashes has been a longstanding problem. As sustainable transport policy becomes prevalent, planners and practitioners will have the opportunity to introduce countermeasures to better meet senior pedestrian needs. In this paper the authors focus on the built environment because this variable category translates into more accessible countermeasures. However, a gap in the literature makes it difficult for planners and practitioners to choose these. Past empirical studies suggest there is an observed risk increase for older adult pedestrians due to their slower walking speed, while crash history studies have yet to provide evidence for this. This gap in the literature begs the question: if there is a link between slower walking seniors and crash incidence. Two models were specified according to younger and older pedestrians involved in crashes that occurred at 191 signalized intersections in Montreal, Canada. The authors sought to determine if older adult pedestrian crash incidence was explained by different characteristics compared to the younger. Results not only showed that older pedestrians were more vulnerable and influenced by some different risk factors than the younger, but that they may be more responsive to some potential countermeasures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".