Design of Real-Time Dynamic Reversible Lane in Intelligent Cooperative Vehicle Infrastructure System
Bibliographic record
Abstract
The rapidly growing traffic demand and the slowly increasing traffic supply have produced an mounting contradiction, which is mainly manifested in cities as road congestion and unbalanced bidirectional traffic flow. Most of the reversible lanes are implemented on fixed sections and fixed times and are mainly guided by ground markings, road signs, railings, and traffic police officer. It requires a lot of human and material costs. And, the control effect is lagging and inaccurate. Aiming at these problems, a real-time dynamic reversible lane scheme in the Intelligent Cooperative Vehicle Infrastructure System (CVIS) was proposed. Traffic information was collected in real time through the CVIS, and a reversible lane scheme was established based on the real-time service level <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>V</mml:mi><mml:mo>/</mml:mo><mml:mi>C</mml:mi></mml:math> and BRP functions. A lane change control model was applied to determine the number of lanes and the timing of lane changes. Then, the reversible lanes were managed in real time through intelligent road stud lights and light curtain walls. Buffer sections and no-entry sections were set to ensure reversible lanes operating safely and efficiently. VISSIM simulation was used for case analysis, and the results showed that compared with the traditional time-controlled reversible lane scheme, the real-time dynamic reversible lane scheme could reduce the average vehicle delay by 27.4% and decrease the vehicle VOC, CO and NO X emissions by 13.5%.
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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 | 0.000 | 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".