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Record W2076632875 · doi:10.1068/b130131p

Road network circuity in metropolitan areas

2015· article· en· W2076632875 on OpenAlexfundno aff
David J Giacomin, David Levinson

Bibliographic record

VenueEnvironment and Planning B Planning and Design · 2015
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsnot available
FundersMcGill University
KeywordsMetropolitan areaTRIPS architectureGeographyFunction (biology)Transport engineeringEconomic geographyBusinessEngineering

Abstract

fetched live from OpenAlex

Circuity, the ratio of network to Euclidean distances, describes the directness of trips and the efficiency of transportation networks. This paper measures the circuity of the fifty-one most populated Metropolitan Statistical Areas (MSAs) in the United States and identifies trends in those circuities between 1990 and 2010. Overall circuity has increased between 1990 and 2010: random points have not only become farther apart in distance, their shortest network path has become more circuitous, suggesting that the more recently constructed parts of street networks are laid out more circuitously than older parts of the network. Over this period thirty-five MSAs experienced a statistically significant increase in circuity (six experienced a significant decrease). As expected, short trips are more circuitous than long trips. A new circuity distance-decay function describes how circuity varies with distance within metropolitan areas. The parameters of this function have changed from 1990 to 2010.

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)0.000
metaresearch head score (Gemma)0.005
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0000.001
Scholarly communication0.0010.002
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 head0.041
GPT teacher head0.214
Teacher spread0.174 · 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

Citations79
Published2015
Admission routes1
Has abstractyes

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