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Record W2580258102 · doi:10.1155/2017/4060348

Analyzing the Influence of Neighborhood Development Pattern on Modal Choice

2017· article· en· W2580258102 on OpenAlexvenueno aff
Alireza Shams

Bibliographic record

VenueJournal of Advanced Transportation · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsModalBuilt environmentTravel behaviorMode (computer interface)LogitMode choiceLogistic regressionField (mathematics)Field surveyComputer scienceTransport engineeringOperations researchGeographyMathematicsMachine learningCartographyEngineeringCivil engineeringPublic transport

Abstract

fetched live from OpenAlex

Although several studies have been undertaken on the association between built environmental characteristics and travel patterns in western societies, the impacts of the local built environment on individuals’ travel behavior considering the specific conditions of developing nations have remained largely unknown. Thus, this paper investigates the travel behavior effects of local planning and design in three residential neighborhoods of Shiraz, a city in the southwest of Iran. The data on land use and built environment characteristics were extracted primarily from an existing digital map and GIS, whereas the data on individuals’ socioeconomics and their daily travel behavior were purposefully collected using a field questionnaire survey ( n=393 ). A nested logit model (NLM) based on the microeconomic utility concept was then applied to discover the impacts of personal characteristics and built environment factors on the choice mode of the individuals. The results and the associated policy implications can be helpful in defining a strategic agenda for neighborhood design and planning.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.316
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2017
Admission routes1
Has abstractyes

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