Dynamic process models of combined traffic assignment and control with different signal updating strategies
Bibliographic record
Abstract
SUMMARY Dynamic process (DP) models of route choice provide a sensible representation of the day‐to‐day evolution of network flows, but their application to the combined modeling of traffic assignment and signal control is not well documented in the literature. In this study, two alternative deterministic, discrete‐time DP models of the interaction between signal control and route choice are proposed and compared with the conventional iterative optimization and assignment (IOA) method for network traffic signal setting. Convergence and equilibrium properties of the two DP models and of IOA are assessed on the basis of extensive numerical tests conducted on a small but realistic network. The frequency of signal resetting is shown to affect significantly the duration of the dynamic process needed to achieve a network flow‐control equilibrium. Our findings also suggest that the realism of IOA may be questioned because of its lack of consideration for important behavioral features such as driver memory and habit. Finally, the possible emergence of instabilities in the DP models is demonstrated through the analysis of bifurcation examples. Copyright © 2012 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 | 0.001 |
| 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".