Impact of Operational Speed Characteristics of Heavy Vehicles on High-Speed Highways
Bibliographic record
Abstract
This thesis explores the safety impact of differential speed limit (DSL) strategy by considering gross vehicle weight (GVW) combined with average speed enforcement (ASE) for heavy vehicles. The study used one-year of Weigh-in-Motion (WIM) data (2014) and one-month of Global Positioning System (GPS) data (Mar 2016) collected from along the Trans-Canada Highway 1 in British Columbia.\nThe research consisted of a data-driven analysis and a two-part simulation analysis. As the DSL investigated was based on GVW, a Modified-Federal Highway Administration (M-FHWA) classification that explicitly considered GVW was tested alongside the FHWA classification regarding average speed and GVW. The simulation analysis assessed the DSL strategy associated with M-FHWA classification and ASE strategys impact on the safety of heavy vehicles.\n In general, the analyses showed that DSL adopted with M-FHWA classes combined with ASE would be effective in reducing heavy vehicle speed and improving highway safety.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".