An alternative definition of dynamic user optimum on signalised road networks
Bibliographic record
Abstract
SUMMARY In the literature on dynamic traffic assignment (DTA), dynamic user optimum (DUO) and dynamic user equilibrium (DUE) are interchangeable and refer to a stable state on a road network in which, under the assumption of perfect information on traffic conditions and the assumption that every traveller chooses the least costly path to travel, no one can reduce his or her travel cost by changing his or her path unilaterally. This paper proposes an alternative definition of DUO on signalised road networks; in such a DUO state, the travel times or costs of used paths between the same origin–destination pair can be different because of the existence of signalised junctions. Usually, on signalised road networks, a DUE solution is sought to approximate such a DUO solution by assuming that the capacity of each approach at a signalised junction is equal to the saturation flow rate of the approach times the corresponding split. By comparing the DUE and DUO solutions of a DTA problem on a small signalised road network, this paper discusses advantages and disadvantages of this approximation and shows that such a DUE solution would not be a good approximation to a DUO solution. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".