Toward a Fair and Efficient Ramp Metering Approach
Bibliographic record
Abstract
This paper introduces a pioneering ramp metering strategy that utilizes utility functions inspired by the concept of fairness, aiming to equitably balance the Total Travel Time (TTT) on the freeway's main flow and the delay experienced at on-ramps. This approach seeks to enhance TTT without unduly burdening individual drivers. To prevent congestion spillback, the model incorporates the maximum capacities of on-ramps as a critical constraint. The study delineates two principal types of novel utility functions: one tailored for each on-ramp and another for individual freeway sections. A Gated Recurrent Unit (GRU) is employed to forecast the inflow to the freeway, enabling a responsive adaptation to the dynamic shifts in traffic flow. The numerical results underscore the model's ability to foster a fair solution in terms of on-ramp queue length and delay without compromising the effectiveness in terms of TTT improvement.
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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".