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Record W606588862

The Impact of Lowered Residential Speed Limits in the City of Edmonton

2013· article· en· W606588862 on OpenAlexaboutno aff
Tazul Islam, Karim El‐Basyouny, Shewkar Ibrahim

Bibliographic record

VenueTransportation Research Board 92nd Annual MeetingTransportation Research Board · 2013
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsPercentileSpeed limitStatisticsPSLMathematicsSimulationEnvironmental scienceTransport engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

In 2010, the City of Edmonton reduced the posted speed limit (PSL) in six residential communities from 50 to 40 km/h. This study investigates the impact of the reduced limit on vehicle speeds using a before-after experimental design with control group adjustment. Continuous speed and traffic flow data were collected at 65 locations for a period of 7 months, including 1 month as a baseline for the before period and 6 months representing the after period. Speed evaluation was performed at several levels ranging from individual speed survey locations to an overall aggregate analysis. Several performance indicators such as mean free-flow speed, speed variance, level of compliance, mean speed of speeders (drivers exceeding the PSL), and percentile speed profile were considered. The results revealed a statistically significant reduction in mean free-flow speed and speed variances for all combinations of time of day and day of week classifications. Though the compliance to the PSL was low after the limit reduction, compliance to a 15 km/h threshold above the PSL was significantly high. Moreover, the analysis showed that the effectiveness of the reduced PSL improved with time.

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)0.002
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 score0.706
Threshold uncertainty score0.584

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.366
Teacher spread0.318 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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