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Record W4237146477 · doi:10.32920/ryerson.14648262.v1

Development of Optimal Control Scheduling for Short-Term Work Zones for Freeway Maintenance

2021· preprint· en· W4237146477 on OpenAlexaff
Udila Shalitha Pilanavithana

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsScheduling (production processes)SolverTransport engineeringWork zoneOperations researchScope (computer science)Work (physics)Computer scienceTotal costControl (management)Operations managementEngineeringBusiness

Abstract

fetched live from OpenAlex

The sustained deterioration of the freeway infrastructure of a nation has resulted in an increase in the number, duration, and scope of maintenance projects. In order to enhance the mobility and safety of freeway segments plagued by work zone activities, transportation agencies and professionals have been exploring the potential benefits of efficient and economical maintenance scheduling. This thesis proposes systematic methodology for the optimization of work zone scheduling based on analytical and simulation models to estimate total project cost. Multi-regression models were developed using microsimulation and embedded them into the costs models and costs were predicted. Solver optimizer was used to find the optimal start and end times, productivity indices, and corresponding sub-sectional lengths of project by minimizing the total project cost. Case studies were conducted to assess the performance of the proposed methodology. Lastly, conclusions were made to support transportation agencies in the development of work zone management plans.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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