Constrained <i>H</i><sub>∞</sub> control of urban transportation network
Bibliographic record
Abstract
Summary Because transportation systems involve massive complex human activities, there exist substantial unpredictable uncertainties of the traffic demands. This paper aims at presenting an H ∞ control method for transportation network that can enhance the tolerance of the system due to these uncertainties. In particular, the store‐and‐forward approach is applied to model the system into a linear form. Then, a detailed controllability analysis shows that the system is not completely controllable by taking the constraints on the green times into account. This makes difficult to apply directly the H ∞ method. To overcome this difficulty, this paper isolates the fully controllable part of the transportation system, and the problem of disturbance attenuation is then solved by means of a convex optimization with linear matrix inequality. Finally, the simulation of a large‐scale hypothetical network is carried out to illustrate the results. Copyright © 2014 John Wiley & Sons, Ltd.
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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".