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Record W4283396548 · doi:10.1080/01944363.2022.2033637

Can We Retrofit Suburban Arterials?

2022· article· en· W4283396548 on OpenAlexaboutno aff
Paul Hess, Michael Piper, André Sørensen

Bibliographic record

VenueJournal of the American Planning Association · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsWalkabilityRetrofittingTransport engineeringSuburbanizationEnvironmental planningUrban planningGeographyBuilt environmentCivil engineeringMetropolitan areaEngineering

Abstract

fetched live from OpenAlex

Problem, research strategy, and findings The multilane arterial roadway is a central feature of post–World War II (WWII) suburbs that challenges efforts to create more transit-oriented regions. Retrofitting suburbs is an important planning goal, but research examining the urban form of arterials and their potential for transformation has been scarce. We analyzed four suburban corridors in the Toronto (Canada) region developed during different periods of suburbanization. We found that the walkability of corridors declined as modernist planning ideas were more fully implemented, and then walkability increased as new urbanist ideas began to influence planning in the 1990s. Over time, however, the retrofit potential declined across all corridors studied, with patterns of lots and development becoming ever more static. Understanding these patterns is important to developing successful strategies for retrofitting suburban arterials.Takeaway for practice Arterial roadway corridors present potential to bring transit-oriented, walkable urban places near large areas of automobile-dependent suburbs and should be a primary focus for retrofitting research and practice. We present here a set of metrics that rely on readily available data, are not complex to carry out, and produce mapping and visualization that is legible and allows comparison across corridors. We suggest that local governments should be routinely studying and evaluating the potential for retrofit and intensification of all such corridors within their jurisdiction. Planners should seek to develop approaches to managing future corridor development to permit greater adaptability in response to future economic, transportation, and climate changes and vulnerabilities.

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 score0.024
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.296
Teacher spread0.277 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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