Dynamically Signal Timing Optimization of Isolated Intersection Traffic Lights Based on a Dual‐Layer Framework
Bibliographic record
Abstract
Intersections are vital components of urban road traffic management, frequently facing persistent congestion challenges. Existing studies rarely combine multiobjective optimization with dynamic adjustment methods. This study introduces an innovative dual‐layer framework for traffic signal optimization. The first layer involves multiobjective optimization, addressing critical performance metrics such as delay, the number of stops, and fuel consumption. In the second layer, we propose a method that uses a fuzzy neural network to learn the correspondence between queue lengths and signal timings. This two‐tiered approach enables real‐time adjustments, achieving dynamic signal optimization. Applying this framework with real traffic flow data to a specific road intersection allows us to determine optimal signal timings dynamically. Extensive simulations using the SUMO software validate the efficacy of our approach in enhancing intersection performance. The timing strategy implemented within this framework leads to a substantial reduction in delay, ranging from 11.1% to 29.0%. The dual‐layer framework presented in this study contributes valuable theoretical insights into future research initiatives in this domain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".