Pavement and Bridge Cost Allocation Analysis of the Ontario, Canada, Intercity Highway Network
Bibliographic record
Abstract
Continuous growth of road transportation demand has resulted in soaring rates of road deterioration and maintenance costs. Full road cost recovery by direct charging of road users is gaining more popularity as governments face pressure to reduce general taxes, and full road cost recovery can promote more efficient use of the road system. A sound charging system requires reliable models for infrastructure deterioration and sound methodologies for cost allocation. In Ontario, new pavement performance models have been recently developed with more emphasis on separating the effects of traffic from the effects of environmental forces on flexible pavements in different geographic locations. The recent models and data have been used to investigate the cost implications of different vehicle configurations and road characteristics for the Ontario pavements and bridges. The results are used for the allocation of costs to various users of the road system. The results generally have implied that initial road design specifications, vehicle configurations, and the types and locations of roads could significantly affect user cost responsibilities. The analyses determined that proper selection of vehicles and payload amounts could result in up to 6 percent savings in pavement costs. The analyses showed that a fair and efficient cost allocation can be achieved by consideration of various vehicle and road characteristics and their interrelated cost implications, rather than solely on the basis of damage implications of each vehicle.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".