Robust control of traffic networks under uncertain conditions
Bibliographic record
Abstract
Abstract Uncertainty of traffic network operations has been a subject of lively debate in the last decade. However, little effort has been put in developing control frameworks that are not only aimed at improving the average performance of the system, but also at improving the system robustness and reliability. In fact, it can be argued that most of the current control approaches are only aimed at improving the efficiency, which can even be counterproductive from a robustness point of view. The main contributions of this article is the proposition of a new control framework based on the notion of controlled Markov processes, which explicitly takes into account the uncertainty in predicted traffic conditions and system performance. Furthermore, in contrast to traditional optimal control approaches, the objective function can include general statistic of the random system performance, such as the mean, standard deviation or 95‐percentile. The contribution aims to make clear how different performance function specifications yield different control strategies. This is shown for a relatively simple case study.
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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".