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Record W3092172397 · doi:10.32866/001c.17408

Speed Limit Changes and Driver Behaviour: A Spatial Lag Model

2020· article· en· W3092172397 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueFindings · 2020
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsInstitut National de la Recherche ScientifiquePolytechnique Montréal
Fundersnot available
KeywordsSpeed limitLagLimit (mathematics)Operating speedTraffic speedComputer scienceTime lagEnvironmental scienceSimulationTransport engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

Speed is a major factor in road safety and the interplay of the different factors affecting speed choice is not completely understood. This paper presents a study of operating speeds in Quebec, Canada, between 2000 and 2018 on provincial highways (except freeways) in response to speed limit changes. This paper shows that driving speed is spatially correlated. Two statistical models are then compared, with and without a spatial component. The spatial model provides a better fit and demonstrates that speed behaviour is spatially correlated. The resulting models confirm that drivers only partly adjust their speeds after a change and that several road features like side curbs, lateral and median strips are associated with driving speed.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.372

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 head0.018
GPT teacher head0.192
Teacher spread0.173 · 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