Dynamic traffic signal control using a nonlinear coupled oscillators approach
Bibliographic record
Abstract
Traditional signal control systems use prestored timing plans that have been developed offline using historic data. These systems are not responsive to dynamical demand changes of traffic and may deteriorate in performance over time. Dynamic traffic signal control systems can adapt to actual traffic conditions, coping with complex flow patterns and unpredictable variations. They seek continuous optimal system performance. The main goal of this paper is to design a methodology for control of arterial traffic flows. To fulfill this goal, the nonlinear coupled oscillators model is adapted to the traffic signal system of a two-way arterial road. The control methodology is based on measurements of the microscopic occupancy parameters for incoming flows at intersections that have a four-way geometrical structure with four-green splits. The desired signal parameters such as cycle times, green splits, and offsets are adjusted dynamically according to local traffic data. Thus, the desired signal patterns are self-organized through the mutual interactions among the signals. The numerical and case study simulation results demonstrate the effectiveness of the control methodology under the dynamical demand changes of traffic. Key words: traffic control, control systems, nonlinear coupled oscillators, signal control.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".