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Record W622854962

Valuation of travel time reliability: a review of current practice

2011· review· en· W622854962 on OpenAlexaboutno aff
Dimitris Tsolakis, Fiona Tan, Tariro Makwasha, Joan-Claire Shackleton

Bibliographic record

Venuenot available
Typereview
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsValuation (finance)Travel timeReliability (semiconductor)Transport engineeringOperations researchTravel behaviorComputer scienceRisk analysis (engineering)Actuarial scienceBusinessEngineeringFinance
DOInot available

Abstract

fetched live from OpenAlex

This report introduces key methodologies and the associated challenges with regards to defining and measuring travel time reliability, in particular, for improving the valuation of travel time in project appraisal by Australian jurisdictions. The numerous studies undertaken in the past internationally and in Australia have been based on existing theories, concepts and definitions; and in existing models and estimation methodology of travel time reliability/ variability. The consensus at the international meeting on ‘value of travel time reliability and cost-benefit analysis’ in Vancouver (November 2009) was that current methodologies are not the most appropriate for analysing travel time reliability/variability; however, they should be used until better methods are developed. The issues and challenges reflected in the findings of this report constitute useful input and a starting point for the work that may be required in Australia to improve on the way of valuing travel time and the importance of the travel time reliability concept. This is an important area of research as travel time savings have tended to dominate economic appraisals of road projects. Estimates of these savings affect decisions about the allocation of available funds between rural and urban roads and between new construction and rehabilitation/ maintenance of existing road networks.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.836
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.063
GPT teacher head0.342
Teacher spread0.279 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2011
Admission routes1
Has abstractyes

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