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Record W2142154381 · doi:10.1139/l06-060

Evaluation of the effectiveness of single-lane two-way traffic control at maintenance and reconstruction zones

2006· article· en· W2142154381 on OpenAlexvenueno aff
Ahmed Al‐Kaisy, Eric Kerestes

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsQueueControl (management)Computer scienceWork zoneParametric statisticsRange (aeronautics)Work (physics)Traffic generation modelTraffic engineeringTraffic simulationTraffic flow (computer networking)SimulationTransport engineeringReal-time computingMicrosimulationEngineeringComputer networkMathematicsStatistics

Abstract

fetched live from OpenAlex

This paper presents a study on evaluating traffic control at single-lane closures on two-lane two-way highways. Four traffic control strategies were investigated by this study. Those strategies involved fixed-time control, fixed-queue control, "static optimum" or convoy rule, and adaptive control. Traffic control strategies were modeled using two approaches; a deterministic approach in spreadsheet application and a stochastic approach in microscopic traffic simulation. Parametric analyses were performed using several variables that are related to traffic control at this type of lane closure. Those variables involved work zone length, average speed at work zone, lost time, traffic level, directional split, and interruptions to traffic by movement of construction vehicle and (or) equipment into and out of the construction site. Study results suggest that significant savings in average delay can be accrued by using more advanced traffic control strategies. Those savings could be as high as 53% for the range of conditions investigated by this research.Key words: work zone, flaggers, adaptive control, simulation, optimization.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.969

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 head0.005
GPT teacher head0.160
Teacher spread0.155 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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