Signal setting with demand assignment: global optimization with day‐to‐day dynamic stability constraints
Bibliographic record
Abstract
SUMMARY This paper deals with traffic signal setting with demand assignment. All approaches proposed in literature to address this problem are based on equilibrium assignment, well established in literature as well as in practice. Still, it is widely acknowledged that there are some relevant issues that may not be effectively addressed under the equilibrium approach, mainly uniqueness and stability, sensitivity to parameters and/or starting state. These issues should be better dealt with a day‐to‐day dynamic approach, through deterministic (or stochastic) process models. This issue seems relevant because optimization of signal timings under equilibrium assumptions may not guarantee that an effective solution is obtained, because it may well be not an attractor of the evolution over time. The main contributions of this paper are as follows: A simple but still effective deterministic process models based on exponential smoothing filters, which also include effects of signal setting, this model allows to state local stability of fixed‐point states (consistent with equilibrium patterns) through the spectral analysis of the Jacobian matrix of the recursive equations modelling the evolution over time of the system. An expression of equilibrium stability conditions that can be included as constraints within global optimization models for signal setting; such models guarantee that stability conditions are satisfied by obtained solution. Results from an application to a toy network, supporting major theoretical findings, are also reported. The very simple example allows for graphical representation to develop a general method useful to address implementation at real scale. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".