Travel demand modeling to simulate traffic loads for pavement deterioration curves: dealing with aggregate data at urban and regional scales
Bibliographic record
Abstract
Traditional pavement management system uses historical data on traffic volume or traffic growth rate to develop the pavement deterioration curves. This study simulates the traffic loads on regional and urban road networks to estimate the pavement deterioration curves applying travel demand models at urban and regional scales during the period of 2013–2062. Highways 1, 2, 7, 15, 16, 102, and 104 connecting the Atlantic Provinces of Canada are considered as the case study at regional scale. Arterial and local roads of both rigid and flexible pavement types in the city of Montreal are considered as the case study at urban scale. The TRANUS model integrates spatial input-output and transportation models to simulate interprovincial freight movement on the regional road network. Urban transportation planning system simulates the urban traffic on the road network of the city of Montreal. The accumulated traffic loads are calculated based on the predicted annual average daily traffic and locally observed truck distributions combined with truck factors. Roughness progression on regional highways and urban roads is estimated by applying regression model of international roughness index (IRI). The IRI will be 35.71, 43.33, 31.62, and 30.67 for flexible-arterial, rigid-arterial, flexible-local and rigid-local roads during the period of 2013–2062, respectively. Comparative evaluation of with and without simulated traffic reveals that the impact of simulated traffic is highest on the pavement structure of Highways 2 and 1 at regional level and rigid-arterial and flexible roads of Montreal. This study improves the traditional method of estimating pavement deterioration by incorporating the simulated traffic and traffic loads into the pavement performance function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".