Pareto optimality control of traffic signal and vehicle speed considering mixed traffic stream at isolated intersection
Bibliographic record
Abstract
Most existing research on the cooperative control of traffic signals and vehicle speed encounters challenges in achieving a balance between traffic efficiency and vehicle fuel consumption at intersections. The pareto optimality control of traffic signal and vehicle speed is proposed to solve this balance problem considering mixed traffic stream at isolated intersection. This control method encompasses three main components: a multi-criteria signal control method, a vehicle speed trajectories multi-objective optimization model based on Pareto optimality, and a macro control strategy. Multi-criteria signal control method is designed based on multiple criteria to divide vehicles into groups optimally. The proposed vehicle speed trajectories multi-objective optimization model is solved by NSGA-II (Non-dominated Sorting Genetic Algorithm II) method to obtain pareto optimality. In order to illustrate the efficiency, this control method is tested in SUMO software compared with ASC (Adaptive Signal Control) method and bilevel optimization method. Average delay and average fuel consumption of this control method are reduced by 44.49% and 25.31% compared with ASC method. On the other hand, this control method also shows obvious balance effect compared with bilevel optimization method under different penetration level and demand level. Furthermore, average delay and average fuel consumption of this control method are reduced simultaneously under 250 veh · l –1 · h –1 level and 40% penetration level, 100% penetration level and 250 veh · l –1 · h -1 level. Experimental results illustrate the effectiveness of the proposed Pareto optimality-based control of traffic signals and vehicle speed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".