Multipath Coordinated Traffic Signal Control of Road Network Based on Improved AM‐Band Model
Bibliographic record
Abstract
A two‐stage multiobjective optimization model for multipath coordinated control based on the improved AM‐Band model is proposed to address issues such as multipath competition and the narrowing of the green‐wave bandwidth in the coordinated control of urban road arterial signals. In the first stage, at the intersections along the arterial road, by considering the analysis of path traffic flows and turning demands, the classical AM‐Band model is improved, and a green‐wave bandwidth maximization model for multipaths is established, aiming to meet the demands of multipath competition to the greatest extent. In the second stage, according to the signal states between upstream and downstream intersections, a reasonable speed guidance range is determined. The vehicle speeds are divided into green light guidance and red light guidance, and a delay minimization model based on optimal speed guidance is established. Furthermore, a multiobjective grasshopper optimization algorithm is used to solve the above models. Finally, four consecutive intersections along Xingguang Road in Xiqing District, Tianjin, are selected for simulation verification. The results of the relevant case studies show that, compared with the Webster scheme and the Yang‐M2 scheme, the average vehicle delay of the model in this paper is reduced by 10.75% and 6.53%, respectively, the average number of vehicle stops is reduced by 43.26% and 16.64%, respectively, and the average travel time is reduced by 10.84% and 3.69%, respectively. This indicates that the model in this study can effectively improve the traffic efficiency of the road network under multipath competition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".