Operational analysis of a connected vehicle‐supported access control on urban arterials
Bibliographic record
Abstract
Urban arterials are characterised by high traffic volume, and driveway densities which cause congestion and crashes. In urban arterials, safety and operational issues can be improved by access management strategies. One such strategy is to restrict traffic entering the urban arterial to ‘right‐in–right‐out’ through implementing a raised median. While past research has shown the operational benefits of this strategy, it has not been evaluated in the context of dynamic access control. This study investigates the effectiveness of the connected vehicle (CV)‐supported dynamic access control. The analysis is applied to an urban corridor under four scenarios: (i) the existing condition with direct left turns (DLTs) permitted at all driveways, (ii) a raised median restricting all driveway traffic to right‐in–right‐out and U‐turns permitted at signallised intersections, (iii) a peak‐hour DLT restriction at all driveways, and (iv) dynamic restriction (i.e. a restriction enforced during the time intervals in which traffic flow rates exceed given thresholds) of driveways to right‐in–right‐out in a CV environment. On the basis of the simulation analysis, it was found that converting driveway access from fully open to right‐in–right‐out based on prevailing traffic conditions in a CV environment can improve traffic operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".