MétaCan
Menu
← Back to cohort

Development of safety performance functions and a GIS based spatial analysis of collision data for the City of Saskatoon

2012· article· en· W128296 on OpenAlexaboutno aff
Jordan Parisien

Bibliographic record

VenueAmerican Journal of Obstetrics and Gynecology · 2012
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsCollisionGeographyEnvironmental scienceComputer scienceComputer security

Abstract

fetched live from OpenAlex

The American Association of State Highway and Transportation Officials (AASHTO) produced the first edition of the Highway Safety Manual (HSM) in 2010. The HSM introduces a six-step safety management process which provides engineers with a systematic and scientific approach to identifying and managing safety concerns on a road network. Each step plays a vital role in improving the safety of target road networks, and the first step, network screening, is the step where the safety issues are first identified. This is accomplished through the use of a series of Safety Performance Functions (SPFs). SPFs are mathematical equations that relate the collision frequency at a particular location with traffic volume and roadway characteristics. The purpose of this research is to develop a series of locally derived SPFs for the City of Saskatoon to allow engineers to estimate the expected number of collisions for the purpose of evaluating new roadway design alternatives and screening the existing roadway network in terms of safety. By developing locally derived SPFs, it may be possible to obtain a better prediction of the expected number of collisions than from using the base model SPFs provided in the HSM. The development of SPFs required three separate databases containing roadway characteristics, traffic volume, and collision records to be integrated into a single database. Using the statistical program R-Language, SPFs were developed and validated for the City of Saskatoon. The developed SPFs were used to conduct a network screening of Saskatoon’s roadways and intersections to identify locations with safety concerns. The results from the network screening were incorporated into ArcGIS to allow for a visual analysis of the spatial collision patterns. Finally, the locally derived SPFs were compared with the HSM base model SPFs to determine if other jurisdictions would benefit from developing their own locally derived SPFs for urban areas.

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 score0.460
Threshold uncertainty score0.925

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.022
GPT teacher head0.240
Teacher spread0.218 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Obstetrics and Gynecology→Same topicTraffic and Road Safety→French-language works237,207→