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Record W2287754250 · doi:10.1680/jtran.14.00090

Reliability of sight distance at stop-control intersections

2016· article· en· W2287754250 on OpenAlexafffund
Said M. Easa, Altaf Hussain

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Transport · 2016
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntersection (aeronautics)Reliability (semiconductor)TruckTransport engineeringReliability engineeringMargin (machine learning)EngineeringRandom variableComputer scienceStatisticsMathematicsAutomotive engineeringPower (physics)

Abstract

fetched live from OpenAlex

The American Association of State Highway and Transportation Officials presents guidelines for intersection sight distance (ISD) based on extreme values of the component design variables such as speed and time gap. The guidelines for a stopped vehicle at a stop-control intersection on a minor road assume that the component design variables are deterministic. This paper presents a reliability method that considers the design variables as random. The method uses the means and variances of the probability distributions of the random variables and accounts for their correlations. A safety margin is defined as the difference between available and required ISD. Relationships for the mean and standard deviation of the safety margin are developed based on first-order second-moment analysis. The proposed method is useful for ISD design of a new intersection or redesign of an existing intersection for a desired reliability level. For single-unit trucks, the obstruction clearances from the minor- and major-road pavement edges are approximately 10% higher than those for passenger cars. The proposed method should be of interest to highway engineers involved in road safety and management.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.395

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 head0.004
GPT teacher head0.166
Teacher spread0.162 · 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 designBench or experimental
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

Citations10
Published2016
Admission routes2
Has abstractyes

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