Utilizing Weigh-In-Motion for Integrated Average Speed and Weight Enforcement (Poster)
Bibliographic record
Abstract
Vehicle safety is a very important issue in North America. This study looks at alternative uses to current transportation technologies. Numerous engineering safety countermeasures are deployed in order to reduce the number and severity of collisions on the road. Speed limits are one example of a traditional method of imposing safety restrictions on the travelling speed of vehicles. Differential speed limits are when different vehicles have a different maximum speed limit imposed on them depending on some established criteria like vehicle classification or gross vehicle weight. Differential speed limits based on gross vehicle weight are difficult to enforce, as current technology capable of effectively and automatically doing this is not being utilized. Weigh-In-Motion, or WIM, are systems designed to capture and record axle weights and gross vehicle weights as vehicles drive over a measurement site. Unlike static scales, WIM systems are capable of measuring vehicles traveling at a reduced or normal traffic speed and do not require the vehicle to come to a stop. This makes the weighing process more efficient, and, in the case of commercial vehicles, allows for trucks under the weight limit to bypass static scales or inspection. This study examines a collection of WIM data collected from two different locations in British Columbia, Canada, and examines the statistical relationships between a vehicle’s speed, classification, and gross vehicle weight (GVW). Data comes from the WIM stations outside of Laidlaw and Golden, spaced 560 km apart located on the TransCanada Highway. Data comes from the year of 2014, and the associated weather data has been obtained from Environment Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".