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Record W2089862386 · doi:10.2495/sdp-v9-n4-568-580

The relationship between built environment and walking for different trip purposes in porto alegre, brazil

2014· article· en· W2089862386 on OpenAlexvenueno aff
Ana Margarita Larrañaga, Helena Beatriz Bettella Cybis

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsTRIPS architectureTransport engineeringRecreationBuilt environmentPublic transportPedestrianLand useVariablesMode choiceTravel behaviorWork (physics)PopulationGeographyBusinessEngineeringEnvironmental healthCivil engineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Measures to encourage non-motorized transport have received increasing attention among congestion mitigation strategies. This paper examines the relationship between walking trips in Porto Alegre and attributes of the built environment, analyzing the effect of trip purpose. To do so, binomial logit models were estimated. Variables were stratifi ed according to mode (motorized and walking trips) and according to trip purpose: work, study and others. Independent variables considered in this research include population density, land use, street design, accessibility of shops and service, and accessibility of public transport and parking supply. This study shows that the effect of urban characteristics depends mainly on the purpose of the trip. On work and study trips, socioeconomic variables have greater predictive power in explaining the decision to walk than the built environment variables. However, on other purpose trips, built environment variables were shown to be stronger predictors. Neighborhoods with mixed land use, grid street networks and shops/services close to households encourage walking for recreational and shopping purposes, whereas free public parking and transit availability discourage this mode.

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 score0.040
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.316
Teacher spread0.280 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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