Evaluation of Accuracy of Weigh-in-Motion Systems in Alberta, Canada, and Its Effects on Pavement Design
Bibliographic record
Abstract
Highway agencies are using progressive Weigh-In-Motion (WIM) systems to collect traffic data for truck overload l enforcement, pavement design and analysis, and operation management. The main advantage of WIM systems is that they can collect various traffic data such as vehicle and axle weights, speed, dimensions, and classifications as vehicles move. WIM measurements, and more specifically their weight measurements, are a function of vehicle and road dynamics. Therefore, WIM weight measurements are different than stationary weight measurements. The accuracy of WIM systems is an important concern for highway agencies. The main objective of this paper was to evaluate the accuracy of WIM weight parameters from a validation testing program conducted on six highway locations from 2006 to 2010 in Alberta. Statistical error analyses were used to investigate the type of errors and their distributions. Additionally, the accuracy of the WIM measurements was evaluated using the American Society for Testing and Materials (ASTM) E1318 probability of conformity standard. Finally, the effect of the errors on pavement design was studied, and the significance of different error scenarios on pavement structure designs was investigated. It was found that WIM measurements in Alberta did not comply with ASTM requirements in any validation year or site location. The significance of WIM errors was estimated to cause up to 44% more pavement damage, which is equivalent to 15 mm of extra asphalt in layer thickness, costing an extra $40,000 per kilometer for a typical two-lane paving project.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".