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Record W67186546

Effect of Geometry of Entrance Terminals on Freeway Merging Behavior

2006· article· en· W67186546 on OpenAlexaboutno aff
Yasser Hassan, Tarek Sayed, Alauddin M Ahammed

Bibliographic record

VenueTransportation Research Board 85th Annual MeetingTransportation Research Board · 2006
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
Fundersnot available
KeywordsMerge (version control)LaggingAccelerationGeometric designUpstream (networking)GeometryComputer scienceTransport engineeringSimulationEngineeringMathematicsPhysicsTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Adequate length of the acceleration lanes allows the vehicles entering into a freeway to accelerate to a desired speed, find an acceptable gap in the right-most through lane, and merge into the freeway safely and comfortably, without interfering with the through traffic movement. The recommendations for freeway entrance terminals geometry in the current design guides seem to include inconsistencies, while research into this area is also lagging. Several aspects of traffic behavior on the freeway merging area were examined using data from 23 entrance terminals in Ottawa, Canada. An acceleration lane length of 425 m was found to be adequate for all geometric and traffic conditions. The study also suggests that tighter geometry of the ramp upstream the gore may not be fully compensated for by a long acceleration lane downstream the gore. Coordination among the geometric elements is rather important in improving the merging behavior and traffic operation at freeway merge 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)0.000
metaresearch head score (Gemma)0.005
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.325
Teacher spread0.309 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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Same venueTransportation Research Board 85th Annual MeetingTransportation Research BoardSame topicTraffic control and managementFrench-language works237,207