Design considerations and traffic performance of freeway automated vehicle managed lanes
Bibliographic record
Abstract
As automated vehicles (AVs) are introduced into the traffic fleet, their operational differences from driver-operated vehicles (DVs) may impact traffic performance and safety. Researchers suggest that dedicated lanes could reduce AV–DV interactions. This paper presents a simulation-based study evaluating the performance of four ML strategies in mixed AV–DV traffic, considering lane position and access control, AV market adoption rate, and ML eligibility. Sixteen ML cases were optimized and compared to a heterogeneous traffic environment based on capacity and efficiency. The results indicated that traffic demand level, MAR, and ML strategy can impact freeway performance. A heterogeneous traffic environment was favoured in most MARs and traffic demand levels. However, one AV left-side ML can be deployed at MARs of 25% regardless of access control, or one AV left-side ML with continuous access at 50% MAR. Implementation of these strategies would involve updating roadway signage to ensure correct usage of the freeway.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".