Examining the Effect of Heavy Vehicles on Traffic Flow During Congestion
Bibliographic record
Abstract
This paper presents an investigation into the effect of heavy vehicles on traffic flow during congestion. Several factors that are thought of as determinants of this effect were considered in this investigation. Empirical data and microscopic traffic simulation were used in the analysis. The simulation model was calibrated and validated using field data at two study sites in Ontario, Canada. One site is located on level terrain while the other is located on a 1-km long 3% upgrade. Simulation experiments were conducted using the calibrated model at the two study sites. The passenger car equivalency factor derived from the queue discharge flow was used as an indicator (measure of effectiveness) of heavy vehicles effect. While study results suggest some similarities between the free-flow and the congested regimes concerning the effect of heavy vehicles, some important differences exist due to the different mechanisms that govern heavy vehicles performance in the two regimes. Also, lane use restriction and the location of bottleneck relative to the upgrade were found to have considerable influence on heavy vehicles effect during congestion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".