Multi-mode traffic network equilibrium and system optimization with tradable credit scheme
Bibliographic record
Abstract
In this paper, we study the equilibrium problem of multi-mode traffic networks (auto and transit networks) with the tradable credit scheme(TCS). We propose a mathematical programming model with the Logit function as the mode-split function to study the users' travel mode and route choice behavior in the multi-mode network under the TCS. Based on the proposed network equilibrium model, Pareto-improving is further investigated. We find that the Pareto-improving under the TCS is a sufficient condition for reducing the system's total cost and we develop a Pareto-improving scheme for specific user categories. For the Pareto-improving scheme, all users do not become worse, and some or all users become better. In addition, a bi-level programming model is also proposed. The upper level minimizes the social total and transit operating costs by optimizing the number of free credits issued and the frequency of transit departure. The lower level is the traffic equilibrium assignment model of the multi-mode networks to determine traffic flows. The model is solved by combining the genetic algorithm(GA) and the heuristic algorithm based on the Method of Successive Averages (MSA). The correctness and validity of the models, propositions, and algorithms are verified using two numerical examples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".