Bi-objective simulation-based optimization for real-time coordinated ramp metering under traffic demand uncertainty
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Bibliographic record
Abstract
This paper proposes a real-time coordinated ramp metering (RCRM) method to simultaneously maximize the number of vehicles entering the expressway mainline from on-ramps and space mean speed of the expressway mainline. This method applies a proportional-differential (PD) controller to adjust vehicular flow entering the expressway mainline from on-ramps. It also utilizes shockwave analysis to dynamically determine the upstream on-ramps that have to be coordinated. In order to ensure the RCRM method can withstand traffic demand uncertainty in real-time, we establish a ramp metering stochastic simulation-based optimization (RMSSO) model to fine-tune the weighting coefficients for on-ramps and PD gains and solve it by a bi-objective surrogate-based promising area search (BOSPAS) algorithm. Simulation experiments in Edmonton show that the optimized RCRM schemes improve the space mean speed of the mainline by around 40% almost without sacrificing the number of vehicles entering the mainline from on-ramps. The outperformance and robustness of the optimized RCRM scheme by BOSPAS are also validated under traffic demand uncertainties.
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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