MétaCan
Menu
Back to cohort
Record W2117676939 · doi:10.5539/mas.v6n9p1

European Speed Environment Model for Highway Design-Consistency

2012· article· en· W2117676939 on OpenAlexvenueno aff
Gianluca Dell’Acqua

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceGeometric designConsistency (knowledge bases)Transport engineeringCurvatureTraffic speedHomogeneousWork (physics)Plan (archaeology)Traffic flow (computer networking)Design speedSimulationMathematicsGeographyEngineering

Abstract

fetched live from OpenAlex

The European road network is very large, and much of it is made up of two-lane rural roads. In Italy the road network requires adjustment work to improve mobility safety management. One of the most important tools for this analysis is the operating speed (V85) profile. In many design standards, different formulations are used for estimating V85, obtained on the basis of research carried out at different times and in different contexts. The aim of the research presented in this study is to analyze driver speed behavior on two-lane rural highways. The study was conducted using traffic counters, able to record in both directions and for every passage of a vehicle, its length, instant speed and direction. The survey plan was elaborated to satisfy different research objectives. The readings were taken by keeping every section under observation for 3, 6 or 12 hours. The data were collected on 11 homogeneous sections of highway. The database consisted of free-flow passenger car speeds and various geometric data from 103 sites. Data were collected at sites to both develop and validate equations. The proposed model includes the radius of horizontal curvature and the “Speed Environment”, which is defined as the speed at which users travel in free-flow conditions when they are not constrained by the alignment of the highway.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.196
Teacher spread0.168 · 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
GenreMethods

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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicTraffic control and managementFrench-language works237,207