A Mechanism for Pricing and Allocation of Mobility Permits on Single-Bottleneck Roadways
Bibliographic record
Abstract
This paper deals with the pricing and allocation problems that arise in a mobility permit (MP)-based traffic management system on single bottleneck roadways. We present a permit endowment (pricing and allocation) mechanism and address the efficiency of such a system in mitigating the congestion at bottlenecks under users' preferences and incomplete information setting. To perform an efficient permit allocation, we develop a mixed-integer programming (MIP) optimization model restrained by user priority and MP availability constraints. For the pricing problem, we present an iterative auction mechanism on top of the MIP-based permit allocation module. We present a performance comparison of the presented mechanism against hypothetical coordinated and centralized efficiency-oriented and equity-oriented systems. Computational results show the potential of the proposed pricing and allocation mechanism for managing MPs in a network with a single-bottleneck roadway where the mobility users, mobility service provider, and system regulator's concerns must be integrated into a MP-based traffic congestion management solution.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".