On the allocation of new lines in a competitive transit network with uncertain demand and scale economies
Bibliographic record
Abstract
Abstract This paper proposes a new model for investigating the allocation of new lines in a competitive transit network in which transit operations are subject to demand uncertainty and scale economies. Scale economies imply that the operating cost of each operator per unit of transit line decreases with the number of lines operated. The proposed model explicitly considers the interactions among three types of players: transit authority, transit operators, and transit passengers. The transit authority aims to maximize the total social welfare for a given confidence level (or probabilistic guarantee) by optimizing the allocation of new lines to bidding operators. For a given allocation scheme, each of the operators determines the associated frequencies and fares to maximize its own profit at a certain confidence level while accounting for the responses of transit passengers to their strategies. The proposed line allocation model is formulated as a robust 0–1 integer programming problem that can be solved by an implicit enumeration algorithm. A numerical example is presented to illustrate the effects on the transit system of the allocation of new lines, the scale economies, the level of variation in passenger demand, and the risk attitude of transit operators toward uncertainty. Copyright © 2011 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".