Bi-objective simulation optimization for online feedback control of variable speed limits considering uncertain traffic demands and compliance behaviours
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Variable speed limits (VSL) stands out as a well-established and effective strategy to alleviate traffic congestion and enhance traffic safety on motorways. It allows variable message signs (VMSs) to dynamically determine the speed limits according to real-time traffic states. This paper introduces an innovative online feedback control approach designed to regulate speed limit values on VMSs, addressing multiple bottlenecks while considering their spatiotemporal constraints. Moreover, we offline optimize the gain coefficients of this feedback control approach in the simulation-based optimization (SBO) framework. Specifically, with average and variance of space-mean speeds as bi-objectives, a stochastic SBO model considering uncertain traffic demands and compliance behaviours is established and solved by a bi-objective surrogate-based promising area search (BOSPAS) algorithm. Real-field experiments conducted in Edmonton, Canada, demonstrate the well-performing bi-objectives of the proposed approach, especially in handling uncertain compliance behaviours and traffic demands. Compared with the uncontrolled scenario, the feedback control schemes with the offline optimized gain coefficients improve the average and variance of space-mean speeds by up to 16.2% and 20.8%, respectively. Meanwhile, by the comparison of detailed performances, it is found that the optimized control schemes perform better than the uncontrolled scheme from the overall and local aspects. In conclusion, this study puts forward a general framework that applies an online feedback control approach with gain coefficients optimized offline by an SBO method to deal with real-time decision-making problems under 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