Passenger car equivalents of trucks on four-lane rural freeways under lane restriction and different traffic conditions
Bibliographic record
Abstract
Truck lane restriction and differential speed limits for trucks and passenger cars are becoming more common policies to improve freeway operations and safety. The most recent edition of the Highway Capacity Manual (HCM) recognizes that the passenger car equivalent (E T ) values may differ with various traffic conditions, but does not explicitly address how E T values may be impacted by truck lane restrictions or differential speed limit policies. This study developed a flow-based methodology to determine E T for trucks under truck lane restriction policies and different levels of demand and traffic composition. A simulation model (VISSIM) was calibrated to reproduce E T in HCM on a level terrain freeway segment and then used to simulate various scenarios to capture the effect of demand flow rate, truck percentage, and compliance ratio to lane restriction, all under the enforcement of differential speed limit policy. The results showed that E T increases as the compliance ratio increases, regardless of the truck percentage and demand flow rate. For a given traffic flow rate, E T decreases as the truck percentage increases. Moreover, regardless of the compliance ratio to lane restriction and the truck percentage, E T increases with the demand flow rate. The statistical analysis revealed that the truck percentage has a significant effect on E T for most cases, except when the truck percentage exceeds 30%. The results also showed significant differences in E T for all demand flow rates at 95% confidence level. The study also developed linear regression models for each level of service to estimate the value of E T as a function of the truck percentage and compliance ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".