Measuring the transportation needs of seniors
Bibliographic record
Abstract
Transportation systems are built with the intention to serve communities by providing accessibility and mobility. Yet seniors residing in these communities face different challenges compared to regular commuters. Seniors have special needs in terms of desired destinations and challenges faced due to limitations in mobility and decline of accessibility levels where they reside. In this research paper we discuss major findings from a mail-out mail-in survey conducted in Hennepin County, Minnesota to measuring met and unmet urban transportation needs of seniors. Compared to previous research this study uses primary collected data rather than relying on travel surveys, which does not measure the unmet urban transportation needs of seniors. The findings from this survey is consistent in term of measuring the existing travel behavior of seniors, which raises our confidence in the information being collected related to the unmet transportation needs of seniors. Seniors are found to be generally independent and rely mainly on auto usage to reach desired destinations at higher rates compared to the rest of the population. The majority of seniors reported although they are currently independent they do know that such independency is not permanent and they have to learn more about alternatives available to them. This study helps transportation engineers and planners in better understanding the current and future challenges that they will face with an aging population.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".