Socially Sustainable Infrastructure Incorporating the Needs of the Aging User in Roadway Design and Upgrades
Bibliographic record
Abstract
Canadian data shows that, even while overall fatal and injury traffic collisions are decreasing, the involvement of aging road users in these serious collisions has grown in terms of both proportion and frequency. The increasing involvement of older road users reflects the increasing number of older persons in the population. The involvement of older persons in collisions can be expected to continue rising as the aging baby boom generation forms an increasing proportion of the driving and general populations, and insists on retaining their driving privileges. This paper and presentation examine ways that an agency can respond to the challenge of maintaining safe mobility for its aging population. The focus of this paper is on engineering measures that can be implemented during the design of new or upgraded infrastructure, or during routine maintenance activities. The paper focuses in part on the initiative undertaken by the Alberta Motor Association to develop the Traffic Safety Engineering Toolbox for Aging Road Users. This Guide adapts and expands the group of measures identified in the US Guidelines and Recommendations to Accommodate Older Drivers and Pedestrians (Federal Highway Administration, 2001) to the Canadian/Albertan driving environment, and incorporates more recent research and best practices. Emphasis is placed on the potential to adopt engineering enhancements on a maintenance basis, so that upgrades to accommodate aging road users are continually and progressively implemented within ongoing maintenance budgets as the population ages.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".