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Record W638645393

Aging Demographics in Medium-Sized Cities: Case Study of Travel Behavior in Kamloops, Canada

2013· article· en· W638645393 on OpenAlexaboutno aff
Erin Toop, Adam Harmon, Eric J. Miller

Bibliographic record

VenueTransportation Research Board 92nd Annual MeetingTransportation Research Board · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsContext (archaeology)SustainabilityBaby boomersGeographyTravel behaviorPopulationEconomic geographyCohort effectTravel surveyGeneration xDemographic economicsDemographyTransport engineeringSociologyEconomicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

In western countries, the imminent aging of the baby boomer generation will have significant impacts on the function and sustainability of transportation systems. While the demographic shift of larger cities will be mitigated by the in-migration of younger residents, smaller cities will experience a sharper increase in median age due to the out-migration of younger residents. This outlook, coupled with the existing auto-oriented culture in medium-sized cities, presents a unique set of transportation sustainability challenges for smaller communities. In the Canadian context, the effects of the aging demographic on transportation demand have received surprisingly little attention, and, though the demographic change will be most pronounced in smaller cities, the existing literature is focused on large cities. Given this, this paper serves to research the effects of age on travel behavior in the medium sized city of Kamloops, British Columbia. From the city’s household travel survey data, it is found that significant travel behavior differences exist between different age cohorts. The empirical behavior analysis is supplemented with the city’s population projections for each age cohort to demonstrate the future transportation impacts of the aging demographics. Though transit ridership in the city is lacking and an increase in older residents is shown to perpetuate this problem, the analysis indicates that the city is well positioned to push for increasing active mode shares into the future.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.397
Teacher spread0.330 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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