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Evaluating Safety of Urban Arterial Roadways

2001· article· en· W2163908274 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Transportation Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTraffic volumeSchema crosswalkTransport engineeringIntersection (aeronautics)PedestrianStatisticsEnvironmental scienceVariablesGeographyMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper describes the development of accident prediction models for estimating the safety performance of urban arterial roadways in the Greater Vancouver Regional District, British Columbia, Canada. The traffic- and road-related variables that appear to underlie the occurrence of accidents are examined and models that explain the occurrence of accidents as a function of these variables are developed. To determine which variables have a significant effect on the safety of urban arterials, the study investigated a large number of models with different combinations of these variables. The investigation led to the conclusion that, among those variables examined, the ones that had a significant effect on accident occurrence were section length, traffic volume, unsignalized intersection density, driveway density, pedestrian crosswalk density, number of traffic lanes, type of median, and type of land use. The models were used to investigate the effect of the median type on accident occurrence. The study estimated that conversion from an undivided arterial to one with a raised-curb median could result, on average, in a 10% accident reduction.

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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