Deep Reinforcement Learning Freeway Controller Chooses Ramp Metering Over Variable Speed Limits
Bibliographic record
Abstract
The benefits of controlling a freeway bottleneck using reinforcement-learning-(RL)-based ramp metering (RM) and/or variable speed limit (VSL) controllers are well established. However, in the event of using both RM and VSL to control the freeway, it is not clear how each method benefits the traffic stream in contrast to the other. We argue that, depending on traffic conditions, it may be better to use one and not both, or more importantly, to dynamically switch between the two. Moreover, a learning agent can automate the switch when warranted. In this paper, we offer intensive analysis and performance evaluations for RL as well as regulator-based RM and VSL controllers applied on both a Aimsun simulated hypothetical freeway network from literature and a real-world freeway on-ramp, extracted from Queen Elizabeth Way (QEW) located in Ontario, Canada with different levels of demand. The findings indicate that RM is more effective and beneficial than VSL in heavily congested scenarios as opposed to VSL, which can be beneficial in moderate and low congested scenarios. We also show that RL has the advantage of automatically prioritizing one control method over the other depending on traffic conditions. We demonstrate that in heavy congestion scenarios, the RL control agent that manages both RM and VSL clearly chooses RM over VSL.
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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) | 0.000 | 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 | 0.001 |
| 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".