Addressing Elderly Mobility Issues in Wisconsin
Bibliographic record
Abstract
The aging of baby boomers poses significant challenges to Wisconsin’s existing transportation infrastructure and specialized transit programs. From 2010 to 2035, the number of elderly Wisconsinites is projected to grow by 90 percent, an increase of 702,760 persons. By 2035, residents age 65 and over will comprise nearly a quarter of the population of Wisconsin, as every county in the state will experience growth in the elderly share of their population over the next 25 years. The U.S. Department of Transportation’s 2003 National Household Travel Survey found that personally-owned vehicles account for over 90 percent of trips taken by elderly residents; the extrapolation of this data suggests an overwhelming majority of Wisconsin’s future elderly residents will be accustomed to driving. Because elderly persons are vulnerable to a decline in visual, cognitive, and psychomotor skills, a dramatic increase in the number of elderly drivers has serious safety implications for the state. Elderly drivers are more likely to have crashes on a per-mile basis, more likely to be at fault in a multicar crash, and more likely to be killed or injured than are younger people in a crash of comparable magnitude. When elderly drivers are forced to stop driving or self-regulate in response to declining abilities and safety concerns, they face increased isolation from social, family, and civic activities and decreased access to medical services. These safety and social ramifications demand an examination of the state’s current driver licensing and education practices, infrastructure design protocols, and specialized and public transit efforts. This report provides analysis of Wisconsin’s existing services, coordinated by the DOT and other State agencies, collects information from elderly residents, and reviews national and international best practices to allow the Wisconsin Department of Transportation (WisDOT) to better manage approaching demographic challenges. Recommendations are provided that include changes in internal structure to address older residents’ mobility concerns, education and outreach opportunities, and development of incentives to provide off prime hour services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".