Dynamic marginal cost, access control, and pollution charge: a comparison of bottleneck and whole link models
Bibliographic record
Abstract
SUMMARY In this paper, we investigate theoretical constructions and properties of three interrelated travel demand management measures including marginal cost pricing, access control, and pollution charge under dynamic traffic assignment framework. For congested traffic networks modeled by the two vertical queue models, that is, the whole link model and the deterministic queuing model, on which flows are controlled, we derive dynamic marginal costs for paths and users' external costs for controlled links. As a strategy to implement the access control, the access pricing is formulated as a dynamic system optimal assignment with access (e.g., traffic volume, queue) control problem, wherein the access constraints represent the restrictions on the traffic volumes and/or environmental constraints. For the whole link model case, an optimal control problem formulation is adopted to investigate the dynamic traffic equilibrium. We derive and discuss the necessary condition for operating the transportation system with capacity/environmental constraints optimally. For the deterministic queuing model case, the inflow to a bottleneck is saturated such that no queue would be formed. The dynamic externalities of the two models are compared. It is found that different externality structures of the two models result in different tolling structures to achieve dynamic system optimal assignment. On the basis of this access pricing analysis and an “equivalent” environmental capacity that converts the environmental constraint into traffic volume restriction, we investigate the traffic‐induced air pollution pricing scheme. 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".