Evaluating Weigh-In-Motion Sensing Technology for Traffic Data Collection
Bibliographic record
Abstract
The significance of highway preservation and budget allocation constraints have motivated development of sensing technologies for collecting accurate and detailed traffic information. While static scales had been used widely to collect vehicle weights, Weigh-In-Motion (WIM) systems have been focused on utilizing state-of-the-art technologies to collect various types of traffic data. These systems continuously collect data, including gross vehicle weights (GVW), vehicle speeds, axle loads, and vehicle classification, as vehicles travel over a set of sensors without interruption of traffic flows. Many up-to-date pavement design protocols require traffic input, and in particular the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) requires axle load, axle spacing, and Average Annual Daily Truck Traffic (AADTT) obtained from WIM. This paper identifies different WIM sensing technologies, with particular emphasis on piezoelectric, bending plate, load cell, and quartz piezoelectric sensor systems. It qualitatively compares the advantages and disadvantages of these WIM systems, with respect to cost, accuracy, applicability, reliability and sensitivity. For the covering abstarct of this conference see ITRD number E216511.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".