A Proportional-Switch Adjustment Model towards Mixed Equilibrium with Multiroute Choice Behaviour Criterion
Bibliographic record
Abstract
Based on the price-quantity adjustment behaviour principle of the non-Walrasian equilibrium theory, this paper adopted a new QUE (quantity adjustment user equilibrium) criterion to formulate the route comfort choice behaviour. The purpose of the present paper is to establish a proportional-switch adjustment model which aims to reflect the route adjustment behaviour interaction between the traditional UE (user equilibrium) travellers and the QUE travellers and converge to a mixed equilibrium state. It is assumed that a group of road network travellers follow the UE criteria by choosing the travel route with the purpose of minimizing their route travel time (travel cost). In addition, the other group of travellers follow the QUE criteria by selecting the route with the largest residual capacity to achieve a more comfortable travel experience. The travel route adjustment behaviour of the two group travellers generates the dynamic traffic flow evolution towards the mixed equilibrium, and the route adjusting flow is proportional to the difference of traveller decision-making variable among the alternative routes. Simple illustrative examples are used to evaluate the performance of the proposed model, and the uniqueness and stability of the solution are demonstrated by applying the variational inequality and Lyapunov stability theorem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".