Assessment of automated vehicles’ freeway exit distances in mixed and managed lane traffic environments
Bibliographic record
Abstract
Vehicles planning to exit at an upcoming freeway off-ramp require adequate exit distance to execute lane change maneuvers, otherwise, traffic disturbances may be experienced. This research assesses the changes in exit distances for a mixed traffic environment comprising automated vehicles (AVs) and human-driven vehicles (HDVs) on freeways with and without a managed lane (ML). A left-side continuous ML was designed and eligible vehicles were AVs. Traffic microsimulation exercises were conducted on a 3.5 km freeway segment, and scenarios varied based on traffic demand, the number of freeway lanes, and AV adoption rates. Traffic demand was set relative to the queue discharge flow rate ( q Q ). The results indicated an increase in q Q as the AV adoption rate increased. Also, the exit distances were influenced by the traffic environment, the freeway configuration, and the traffic demand. The optimal exit distance increased when an ML was implemented at 25% and 50% AV adoption rates.
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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 | 0.001 | 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) | 0.000 | 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".