Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning
Bibliographic record
Abstract
Highway merging bottleneck is challenged with serious traffic conflicts between on-ramp and mainline vehicles, causing significant capacity drop and drastic speed changes. The paper proposes an adaptive coordinated variable speed limit model to manage highway speed of on-ramp and mainline continuous sections without priority to mainline. That helps to remove the speed difference between the vehicles from on-ramp and mainline flooding into the merging zone, and to sustain actual traffic density close to critical density to counteract capacity drop as indicated with macroscopic fundamental diagram. The method of deep reinforcement learning based on deep deterministic policy gradient is employed to solve the proposed model with a row of continuous control variables. Simulation platform with VISSIM 5.3 is established, and the proposed method can enhance traffic flow through the merging zone by around 10% and 19% under static and dynamic demand, respectively, in addition to reduced density and speed variation by around 30%. This research provides insights into the management of highway capacity so as to secure traffic efficiency and reliability for the merging zone.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".