Signal Control Strategy for Supersaturated Traffic at Urban Road Intersections with a Large Technical Grade Gap
Bibliographic record
Abstract
To reduce the risk of queuing overflow on the urban minor road at the intersection under supersaturation where the capacity of the arterial and minor roads shows extreme disparity, reduce the adverse effects caused by long queues of vehicles on the minor road, and comprehensively balance the multiobjective requirements such as priority of the main road, queuing restrictions, and delay on the minor road, the minor road queue model at the end of red, a road remaining capacity model, and multiparameter coordinated signal control model were established, and a multiobjective genetic algorithm was used to optimize this solution. As an example, the multiparameter coordinated control strategy decreased the delay per vehicle by approximately 17% and the queue length by approximately 30%–50% on the minor road and slightly increased the delay per vehicle at the intersection and the length on the main road queue. This control strategy can make full use of the capacity of the main road to control the queue length on the minor road, effectively reduce the risk of minor road queue overflow blocking local road network traffic operation involved, and comprehensively balance the traffic demand between arterial and minor roads. It provides a reference control method for coping with the transfer of the main traffic contradiction under the oversaturated state of the road intersection with large disparity.
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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".