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Record W2048136858 · doi:10.1139/l08-020

Evaluating new methodologies for estimating performance on two-lane highways

2008· article· en· W2048136858 on OpenAlexvenueno aff
Ahmed Al‐Kaisy, Casey Durbin

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersMontana State UniversityUniversity of Montana
KeywordsHeadwayHighway Capacity ManualTransport engineeringEstimationSensitivity (control systems)Current (fluid)StatisticsComputer scienceEngineeringMathematicsLevel of service

Abstract

fetched live from OpenAlex

This paper examines two new methodologies for field estimation of percent time spent following (PTSF) on two-lane highways. Using speed and time headway data, the two methodologies estimate the percentage of vehicles trapped in platoons, unable to pass slow-moving vehicles. A total of 236 h of field data, including individual speeds and headways, were collected at three study sites in the state of Montana. The study examined the sensitivity of PTSF estimates to some important platooning variables. The study also included a comparison of PTSF estimates from the new methods versus those found using the current United States National Research Council Highway capacity manual (HCM) analytical procedures. Study results showed that the two new methodologies were successful in capturing the effect of platooning variables on PTSF estimates and outperformed the HCM procedures in this regard. Further, the results strongly attest to the promise of the proposed methods in advancing the current analytical procedures for estimating performance on two-lane highways.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.732
Threshold uncertainty score0.656

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 head0.074
GPT teacher head0.275
Teacher spread0.201 · 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

Citations29
Published2008
Admission routes1
Has abstractyes

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