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Record W125594666

How Does Land Use Influence Cyclist Route Choice? Geospatial Analysis of Commuter Routes and Cycling Facilities

2011· article· en· W125594666 on OpenAlexaboutno aff
Brian H. Y. Lee, Lance Jennings, Ahmed El-Geneidy

Bibliographic record

VenueTransportation Research Board 90th Annual MeetingTransportation Research Board · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsGeospatial analysisCyclingTransport engineeringLand useSample (material)Work (physics)Resource (disambiguation)GeographyVariety (cybernetics)Environmental resource managementComputer scienceEnvironmental planningEnvironmental scienceCartographyEngineeringCivil engineeringForestry
DOInot available

Abstract

fetched live from OpenAlex

This paper contributes to the body of literature on the built environment and non-motorized travel behaviors by examining the role of land use in cyclist route choice. Using data from a sample of cyclists in Montreal, Quebec, Canada who responded to a web-based survey, the routes for those who were traveling for work or school purposes and used cycling facilities were examined and these actual taken routes were compared with the corresponding shortest-path routes with respect to the adjacent land use. By using a variety of geospatial analysis tools, different methods to quantify land use, including area- and count-based measures, were examined. A series of statistical tests and models revealed that commuter cyclists prefer to ride through areas that are generally less busy and have lower potentials for conflicts. This includes routes that have adjacent residential as well as resource and industry uses and paths that are near water.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0020.004
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.379
Teacher spread0.287 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2011
Admission routes1
Has abstractyes

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