Bi-objective simulation-based optimization for real-time coordinated ramp metering under traffic demand uncertainty
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".