Simulation Framework for Analysis of Elderly Mobility Policies
Bibliographic record
Abstract
The population in developed countries is aging. Literature has paid minimal attention to the effects that population aging may have on transport demand and sustainability. These effects are believed to be more pronounced in urban areas. A policy framework is presented. It can be used as a template for the development and evaluation of policies that directly or indirectly relate to the effect of population aging on transport in urban areas and thereby inform the planning process. A component of the proposed policy development framework is an urban transport simulation model that is used to simulate policy scenarios over time. Integrated Model for Population Aging Consequences on Transportation (IMPACT) is a conventional transport simulation model coupled with a powerful demographic model that has the potential to project the population of traffic analysis zones over time, as it takes into account vital statistics (births, deaths) and migration rates. Two types of policies were investigated. The first regards elderly driver's license renewal; the second is related to new housing development policies. The results indicate that such a system can produce results that can inform policy regarding elderly automobility.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".