Research on Platoon Dispersion Delay of Traffic Flow considering Coordinated Control
Bibliographic record
Abstract
To improve the traffic capacity and reduce the delay of signalized intersections, the delay of coordinated control intersections is studied. Based on the freedom and randomness of the speed change and considering the delay problem caused by the discrete behavior, the authors deduced a new delay model. Firstly, by analyzing the kinematic behavior of traffic flow under coordinated control, it is found that traffic flow reaches the downstream intersection in two different forms. The two forms were as follows: the tail vehicles of discrete traffic flow were truncated and the front vehicles of discrete traffic flow were stopped. Then, the authors deduced the new delay model by analyzing the two conditions. Finally, the delay of the two cases is analyzed, which can be used as the basis for setting the phase difference between coordinated control intersections. The correctness of the model is verified by designing two example coordinated control intersections under unsaturated flow with MATLAB. Results show that the discrete traffic flows will have different impacts on delay or traffic efficiency when they arrive at the downstream intersection in different forms. Through the analysis of the delay of vehicles, when the green split is less than 0.64, the tail truncation delay is greater than the front truncation delay. When the green split is greater than or equal to 0.64, the opposite is true. The phase difference of upstream and downstream intersections can be optimized and coordinated according to the goal that vehicles can smoothly pass through the coordinated control intersection or ensure the minimum delay, so as to give full play to the space-time utilization of the coordinated control intersection.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".