Probabilistic Methodology to Quantify User Delay Costs for Urban Arterial Work Zones
Bibliographic record
Abstract
The total cost of road construction comprises direct costs paid by agencies and indirect costs paid by road users. In densely populated urban areas, work zone instigated user costs could outweigh direct costs and therefore need to be considered in project alternative selection. This paper develops and applies a probabilistic methodology for quantifying user delay cost (UDC) for urban arterial work zones. The methodology incorporates traffic microsimulation and Monte Carlo simulation to establish a distribution of UDCs, which supports risk-based optimization of work zone configuration, justification of accelerated construction methods, and establishment of contractual incentives and disincentives. The paper demonstrates the development of the methodology through a bridge rehabilitation case study in Calgary, Alberta, Canada. The results revealed that every hour of work zone operation during the morning peak resulted in 169.2 h of network-wide vehicle delay and a mean UDC of CAD 2,816 (in 2016 Canadian dollars). To further demonstrate the applicability of the methodology, a second case study examined three work zone configurations and concluded that the traditional work zone configuration instigated the lowest UDC.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".