Modeling the performance of restricted crossing U-turn intersections including the effects of connected and autonomous vehicles: a case study in California
Bibliographic record
Abstract
Despite numerous studies demonstrating the effectiveness of restricted crossing U-turn (RCUT) intersection design, its implementation remains close to zero in some large states (e.g., California). The study included four locations on high-speed rural expressways (highways with partial access control) in California. The operational evaluation relies on microscopic simulation models of existing two-way stop-controlled (TWSC) intersections and alternate RCUT designs used to estimate network-wide performance measures. Two series of simulation scenarios were tested: (1) scenarios with no connected and autonomous vehicles (CAVs) and (2) scenarios with different SAE International Level 4 CAV market penetration rates (MPRs) to help policymakers and practitioners in future planning strategies. The microsimulation models were also used to generate trajectory data to create a surrogate measure-based approach. Based on the results, the RCUT designs reduced or eliminated the more severe crossing conflicts. Similar vehicle travel times were identified in various CAV MPRs except for one of the locations with challenging geometric features, where travel time increased at higher CAV MPRs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".