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Record W201756761

Analysis of 110 km/hr Speed Limit: Implementation on Saskatchewan Divided Rural Highways

2004· article· en· W201756761 on OpenAlexaboutno aff
P B Hunt, B Larocque, Wayne G. Gienow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsSpeed limitPercentileLimit (mathematics)Environmental sciencePaceStatisticsTransport engineeringMathematicsEngineeringGeographyGeodesy
DOInot available

Abstract

fetched live from OpenAlex

This paper will assess the short-term on driver speeds after increasing the posted speed limit on rural four-lane highways in Saskatchewan. On June 1, 2003 the maximum speed limit on select sections of Saskatchewan twinned highways was increased from 100 km/hr to 110 km/hr. Spot speed studies were conducted at representative locations before and after the speed limit increase, during the period from April 2003 to September 2003. While the majority of the data collected was on the four-lane highway system, data was also collected on two-lane highway sections to identify any possible halo effects where speed limits were not increased. Data collected at the study sites before the increase indicated that the 100 km/hr speed limit was well below the average driver speed and 85th percentile speed. Data collected after the speed limit increase showed only a minimal increase in average driver speeds, 85th percentile speeds, and pace speeds, while the average increase in speed differential was found to be minimal. The speed limit increase appears to have created a higher driver compliance rate, at least in the short term. The increased compliance rate does not necessarily reflect a change in driver behavior, but may be the result of a change in how compliance is measured. Speed profiles on two-lane highways only showed small changes in vehicle speed characteristics. Overall, raising the posted speed limit has had a lesser effect on driver speeds than anticipated. Due to the short duration of the study, additional studies are recommended to further identify vehicle speed trends and possible changes in collision profiles. For the covering abstract of this conference see ITRD number E211395.

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)0.000
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 score0.681
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2004
Admission routes1
Has abstractyes

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