Simulation and Evaluation of Automated Vehicle Identification at Weigh-in-Motion Inspection Stations
Bibliographic record
Abstract
This paper describes the development and validation of a discrete event microsimulation model that was applied to investigate the implementation of automated vehicle identification (AVI) technologies at Nordel Inspection Station in Delta, British Columbia. Current operational policies require commercial vehicles passing through the area to be inspected. The study, which includes an extensive field survey to collect validation data, determined that the implementation of a conservative industry participation of 10% in the AVI program would result in benefits ranging from $2.4 million to $7.9 million (2008 CAD), or benefit–cost ratios ranging from 11 to 47 for a range of net-present value project costs of $50 to $200,000. Benefits also include the reduction of emissions, with 5-year greenhouse gas (GHG) emissions reductions ranging from 2,200 to 7,200 metric tons, or a cost of $9.1 to $123 per reduction of 1 ton of GHG, for a range of project implementation scenarios.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".