Modeling and control of variable approach lanes on an arterial road: a case study of Dalian
Bibliographic record
Abstract
Unbalanced directional traffic, which often exists at intersection approaches, is an important factor to induce traffic congestion on urban streets. Considering the settings of variable approach lanes, the presence of left-turn bays, and a variety of vehicle categories on an arterial road, an optimization model that minimizes the total delay is formulated and a control method that coordinates a variable sign and the corresponding signal group is put forward. To design the signal control scenarios with time of day, the procedure for using the proposed methodology is also presented in practice. To verify the given methodology, a case study is implemented using the field data and the four scenarios. The results reveal that the new methodology can better respond to the time-varying traffic flow at intersection approaches, and the provision of variable approach lanes and left-turn bays are helpful to reduce the average delay and enhance the average speed.
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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".