Incorporating User Delay Cost in Project Selection: A Canadian Case Study
Bibliographic record
Abstract
Multi-million dollar contracts, economic impact on industries and public satisfaction are factors agencies, dealing with transportation infrastructure, must deal with regularly. It is clear that construction is necessary to maintain or upgrade the current roadway infrastructure in Canada. The long-term result is a smooth, comfortable and safe road for motorists. Short-term effects, especially to the traveling public, will be difficult, primarily through delays and an increase in vehicle operating costs. Determination of such costs may be performed at the project level. However, this information is rarely implemented into the selection of the best alternative treatment, leading to potential disaster to the traveling public at the onset of construction. The cost of delays associated with freeway repairs and upgrades is typically borne by the motorists themselves. As a means of alleviating public frustration, the costs that have been relegated to the public are being incorporated into the project selection. This will allow the agency to select an alternative that will be economically feasible with minimized public impact. The purpose of this study is to analyze the user delay cost not only during construction on an existing facility, but the delays caused by routine maintenance activities. A computer model
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".