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Measuring the transportation needs of seniors

2012· article· en· W2112847447 on OpenAlexaff
Rania Wasfi, David Levinson, Ahmed El-Geneidy

Bibliographic record

VenueJournal of Transport Literature · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcGill University
Fundersnot available
KeywordsDestinationsTransport engineeringPopulationBusinessTravel behaviorMarketingGerontologyPsychologyEnvironmental healthGeographyMedicineEngineeringTourism

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.268
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2012
Admission routes1
Has abstractyes

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