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Record W1991486857 · doi:10.5539/jsd.v3n4p38

Exploring Effects of Area-Wide Traffic Calming Measures on Urban Road Sustainable Safety

2010· article· en· W1991486857 on OpenAlexfundvenueno aff
Anna Granà, Tullio Giuffrè, Marco Guerrieri

Bibliographic record

VenueJournal of Sustainable Development · 2010
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
FundersUniversità degli Studi di PalermoTransport Canada
KeywordsTraffic calmingTransport engineeringContext (archaeology)Road trafficResidential areaGeographyCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Traffic calming schemes refer to a combination of road network planning and engineering measures to minimize undesirable effects of traffic in residential areas. The traffic calming role in urban road network management is, indeed, to enhance road safety as well as other aspects of liveability for the citizens; in this context accident reduction can be a realistic objective. Several studies highlight that traffic calming treatments can significantly reduce road accidents in urban areas. Nevertheless, the increase of the accident rate per kilometre travelled has been found in urban areas as result of the so-called accident migration phenomenon. Starting from these considerations, the paper discusses the effects of traffic calming measures on road safety. The paper also aims to provide a concise overview of knowledge on the potential of the meta-analysis method in detecting the true effect of traffic calming measures on road safety. Therefore, the role of the road network planning and the characteristics of urban road network that have to be consistent to the traffic calming objectives are examined. Finally, authors suggest a methodological procedure for implementing a traffic calming zone in residential areas, from planning level to road design.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.191
Teacher spread0.177 · 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.

Study designSimulation or modeling
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

Citations18
Published2010
Admission routes2
Has abstractyes

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