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Record W205518246

Follow-up Study on Pedestrian Scramble Operations in Calgary, Canada

2010· article· en· W205518246 on OpenAlexaboutno aff
Manoj Shah, Lina Kattan, Richard Tay, Shanti Acharjee

Bibliographic record

VenueTransportation Research Board 89th Annual MeetingTransportation Research Board · 2010
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsPedestrianPoisson regressionTransport engineeringPedestrian crossingGeographyEngineeringDemographyPopulationSociology
DOInot available

Abstract

fetched live from OpenAlex

Pedestrian scramble operation (PSO) is an exclusive pedestrian signal phase where traffic on all four directions is stopped and pedestrians are allowed to make lateral as well as diagonal crossing. PSO was implemented at two intersections in the City of Calgary in May, 2008. An earlier study analyzed its effects on pedestrian violations and vehicle-pedestrian conflicts six weeks after the implementation of the scramble operation. This paper is a follow up study to determine the longer term effect of this new operation on pedestrian safety. Field observations were again taken one year after the implementation of PSO. Four Poisson regression models were developed to model the number of conflicts and violations. We found some changes in the results from the previous study conducted at the same location. Our results showed that the number of pedestrian-vehicle conflicts and pedestrian violations decreased significantly on weekdays but both pedestrian violations and conflicts increased significantly on weekends after implementation of the scramble operation. Our analysis also revealed that 1.1% of No Right Turn on Red (NRTOR) was violated on weekdays and 3.1% of NRTOR were violated on weekends. The study also looked crashes from three years before and one year after the implementation of PSO and found no significant change in the number of crashes per year. This result is not surprising since none of the crashes recorded involved pedestrians.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.004
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 head0.039
GPT teacher head0.336
Teacher spread0.297 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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