Simulating Freight Traffic between Atlantic Canada and Québec to Support Pavement Management on New Brunswick’s Regional Highways
Bibliographic record
Abstract
Traffic loading for pavement deterioration should be modeled as a dynamic indicator based on trip distribution derived from spatial economics. The estimation of modal distribution of trips and land development has been the main focus of integrated land use and transport models. However, no connection with transportation asset management has been established. This paper proposes the use of spatial economic simulation to forecast freight-traffic distribution to improve pavement-deterioration modeling. A case study of trade flows between Canada’s Atlantic Provinces and Québec is used to show the pitfall of current management models in estimating rates of deterioration, underfunding maintenance, and rehabilitation strategies. It was found that a total cost of $25 million could maintain adequate levels of condition under the current performance modeling; however, such a budget is inadequate when performance is based on forecasted truck traffic. It was also found that aggregation of pavements in a few homogeneous groups resulted in the inability to prioritize investments considering the economic relevance of the road in the region. This study suggests the use of individual deterioration models for strategic roads.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".