Coupled Control of Traffic Signal and Connected Autonomous Vehicles at Signalized Intersections
Bibliographic record
Abstract
To enhance the traffic operation efficiency at signalized intersections, a model for coupled control of traffic signals and connected autonomous vehicles at isolated signalized intersections is proposed. This model estimates the time that CAVs reach stop lines with real-time information about the speed and position of CAVs. The arrival time is leveraged to optimize traffic signal timing by rolling horizon, with the maximization of phase saturation as the optimization objective. Based on the optimized traffic signal timing, the speed profile of CAVs is optimized by a linear integer programming, with the maximization of speed at the moment of reaching the stop line as the optimization objective. Through the coupled control of travel speed and the traffic signal, CAVs can pass through the intersection safely, efficiently, and smoothly. NetLogo, a multiagent microscopic simulator, is developed to test this strategy, and an intersection in Weihai is taken for verification and analysis lastly. The simulation results demonstrate that, compared with the fixed traffic signal timing control and the model optimizing only speed profile of CAVs, the proposed model can reduce the average number of stops by 47.0% and the queuing time by 41.3%. In addition, the optimization is better during off-peak hours, about 10% higher than the peak hours.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".