Multi-Intersection Control with Deep Reinforcement Learning and Ring-and-Barrier Controllers
Bibliographic record
Abstract
This paper discusses a machine-learning traffic signal control method. A full-scale corridor is analyzed and the transferability of using a model pre-trained on a single intersection is examined. Two controller designs are explored, a simple two-phase design and a full ring-and-barrier style controller. The full ring-and-barrier controller adapts many of the key features present in traditional controllers, such as protected-permissive left turns, so that they can be used in the reinforcement learning (RL) paradigm. This study is the first to propose a method that uses deep reinforcement learning (DRL) to implement a full ring-and-barrier style controller. The study also examines the feasibility of using transfer learning to pre-train a model on a single intersection and then fine-tune it for application in a complete environment. Training is done on a simple four lane intersection and the pre-trained model is then transferred for fine-tuning to six controllers operating on a corridor modeled with field data obtained for University Avenue in Waterloo, Ontario, Canada. The performance of the fully trained model is then compared with the existing signal plans in relation to the average delay and average queue length. Application of the ring-and-barrier design to this corridor was found to reduce delays by at least 5% and average queue lengths at intersections by 27%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".