USER DELAY IMPACTS OF ALTERNATIVE TRAFFIC PLANS FOR MAINTENANCE AND REHABILITATION INTERVENTIONS
Bibliographic record
Abstract
Whether or not user delay costs due to maintenance and rehabilitation (M&R) interventions should be included in life cycle analysis is a controversial subject. However, there is a growing awareness or acceptance by public agencies that they should be considered either directly, in life cycle analysis, or at least indirectly in terms of the represented by delays. In fact, there is some contention that this down time is no different than that occurring when workers are not on the job due to illness, etc.A number of quite sophisticated user delay models have been developed which calculate slowing plus queuing delays, and the associated costs. A comprehensive model was developed, Ontario Pavement Analysis of Costs (OPAC) 2000 pavement design system, as reported at the 8th Conference in Seattle in 1997. This paper first identifies the available models, their principal features and their limitations. Among the limitations for most of the sophisticated models is the extensive requirement for input data, which in turn makes them cumbersome to use. The real issue suggested in the paper is twofold: (a) obtaining quantitative numbers on delay times and costs of sufficient reliability for the life cycle analysis, and (b) being able to evaluate the alternative traffic plans, including detours. To address these issues, the paper shows how user delay calculations can be simplified to yield approximate but still sufficiently reliable numbers for life cycle comparison of alternative M&R strategies as well as comparisons of a wide range of alternative traffic plans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".