Weigh-in-Motion Applications for Intelligent Transportation Systems-Commercial Vehicle Operations: Evaluation Using WESTA
Bibliographic record
Abstract
An investigation was undertaken to sort the efficiencies of different types of weigh-in-motion (WIM) systems commonly used for enforcement of commercial vehicle operations. Weigh station microsimulation model WESTA (WEigh STAtion) was used. The investigation focused, in particular, on the effect WIM system accuracy has on the effectiveness of presorting commercial vehicles before they approach a weigh station. WESTA simulations were performed, with and without mainline WIM, on a typical commercial weigh station facility across a range of commercial truck volumes (200, 400, and 600 Class 9 trucks per hour) and WIM system accuracies (ASTM Type III and Type I WIM). Three evaluation criteria were used: ( a) number of compliant trucks required to report to the station, ( b) number of overweight trucks instructed to bypass the station, and ( c) time the weigh station remained open. It was found that weight enforcement efficiency improved with WIM. The improvements in efficiency translate into considerable savings for both the weight enforcement agency in relation to improved enforcement effectiveness and protection of the infrastructure and for the trucking industry in relation to reduced user-delay costs. It was also found that higher WIM system accuracy results in higher agency and user savings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".