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Record W1971199277 · doi:10.3141/2247-13

Much-Anticipated Marriage of Cycling and Transit

2011· article· en· W1971199277 on OpenAlexafffundabout
Julie Bachand-Marleau, Jacob Larsen, Ahmed El-Geneidy

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsCyclingPublic transportTransport engineeringTransit (satellite)TRIPS architectureSustainable transportRail transitEconomic shortageMetropolitan areaTravel behaviorBusinessSustainabilityEngineeringGeographyGovernment (linguistics)

Abstract

fetched live from OpenAlex

In response to the environmental, economic, and social costs associated with overreliance on the automobile, planners and transportation professionals are promoting sustainable alternatives such as walking, cycling, and public transit, either as single modes or in combination. It has been argued that the marriage between cycling and transit presents opportunities for synergy by enlarging catchment areas of transit stations while drawing in new users to both of these green modes. However, because of the marginality of combining cycling and transit in North America, there is a shortage of reliable empirical studies in this area. The present study addressed this gap through an analysis of travel behavior and preferences related to cycle–transit (C-T) integration. An online survey was conducted in the region of Montreal, Canada, during the summer of 2010. The questionnaire included a section on Montreal's public bicycle sharing system, Bixi (bicycle taxi), and its potential for integration with transit. Three current or potential C-T user groups were identified through a factor–cluster analysis: current parking bike-and-riders, Bixi users, and car drivers. Bringing a bicycle on transit was the preferred form of integration; however, scenarios involving bicycle parking (or using a public bicycle) were likely to be used more regularly. To accommodate the greatest number of bicycle–transit trips, measures that facilitated parking at transit stops and those that enabled the bringing of bicycles on board transit vehicles were recommended in tandem.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.220
GPT teacher head0.435
Teacher spread0.215 · 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

Citations123
Published2011
Admission routes3
Has abstractyes

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