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Transportation Activity Centers for Urban Transportation Analysis

2006· article· en· W2133493492 on OpenAlexaff
Jeffrey M. Casello, Tony Smith

Bibliographic record

VenueJournal of Urban Planning and Development · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMetropolitan areaUrban agglomerationTransport engineeringRegional scienceStrengths and weaknessesGeographyEconomic geographyUrban structureCenter (category theory)Environmental planningBusinessUrban planningCivil engineeringEngineeringPsychology

Abstract

fetched live from OpenAlex

North American cities have been evolving from monocentric to polycentric metropolitan regions, with multiple concentrations of activities occurring outside of traditional urban cores. This new urban structure, with multiple urban “activity centers,” has profound influence on the functioning of cities, particularly transportation systems. This paper reviews the development of “activity center” definitions from various research fields, and concludes that the application of contemporary definitions to transportation analyses has significant weaknesses, particularly in analyzing suburban agglomerations of activities. An alternative method is suggested which incorporates not only the presence of concentrated employment, the traditional criteria, but also the trip-attracting strength of the employment types present in an activity center. The Philadelphia metropolitan area is analyzed using traditional definitions and the proposed methodology; several areas which would not be identified using traditional definitions meet the new criteria. The impact of these “transportation activity centers” on Philadelphia’s travel patterns is explored.

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)0.001
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 score0.037
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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

Citations26
Published2006
Admission routes1
Has abstractyes

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