Enhancing and Evaluating a Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Controller on an Isolated Signalized Intersection
Bibliographic record
Abstract
Effective traffic signal control strategies are essential for optimizing urban traffic flow and adapting to dynamic traffic patterns. This research evaluates the performance of the Laguna-Du-Rakha (LDR) developed cycle length optimization strategy and enhances a decentralized cycle-free Nash bargaining (DNB) adaptive traffic signal controller. The enhanced DNB controller utilizes traffic measures, such as queue lengths and approach densities, to optimize phase sequences and splits, considering each signal phase as a player in a game-theoretic framework. Both methods are implemented at a simulated isolated intersection in Toronto, Canada. Comparative performance analysis demonstrates superior performance of both optimization methods over the state-of-practice, the Webster method. Specifically, the optimized cycle length method achieved a 25.6% reduction in delay and a 5.9% reduction in fuel consumption. The DNB controller produced a 37.7% reduction in delay and a 7.4% improvement in fuel economy, alongside significantly decreased queue lengths at intersection approaches for both methods. The optimized cycle length method mitigates the overestimated cycle lengths produced by the Webster method, leading to enhanced control performance. Furthermore, the application of game theory to traffic signal control provides dynamic switching behavior that adapts to fluctuating traffic conditions, thereby reducing traffic congestion and improving fuel economy in urban networks. As such, this research contributes to the development of smarter and more responsive urban traffic management systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".