Reinforcement Learning–Based Ramp Metering Strategy Considering Queue Management
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Bibliographic record
Abstract
This paper introduces an action replacement module for reinforcement learning (RL)–based ramp metering to address the issue of ramp queue spillback during the training process. Ramp queue spillback leads to significant impacts on the traffic efficiency of adjacent road networks, making it a critical concern in ramp control. Existing RL approaches often employ ramp states as reward functions to encourage agents to learn strategies that avoid queue overflow. However, due to the trial‐and‐error nature of RL, these methods frequently generate actions that cause queue spillback during training, posing challenges for real‐time online training in real‐world applications. To overcome this limitation, the proposed action replacement module utilizes the store‐and‐forward model to estimate a lower bound for ramp metering rates. By identifying and replacing actions that fail to meet this constraint, the strategy effectively prevents queue spillback. In addition, penalties are imposed on replaced actions to guide the agent in learning effective and practical control policies. The proposed method is evaluated in both single‐ramp and multiramp scenarios. Experimental results demonstrate that the agent can learn the queue spillback prevention strategies, and nearly eliminate ramp queue spillback without compromising control performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it