Modeling and managing congested transit service with heterogeneous users under monopoly
Bibliographic record
Abstract
We develop and study a model of congested transit service under monopoly when potential users differ in their characteristics. Given travel time delays and crowding externalities, general user heterogeneity is characterized in a three-dimensional space of value of time, value of crowding, and willingness to pay. A unique user demand equilibrium is shown to exist. The operator chooses the fare and service frequency to maximize a weighted sum of profit and consumers’ surplus. The socially optimal fare consists of marginal operating cost, external user congestion cost, and a nonnegative shadow price on the vehicular capacity constraint which may or may not bind. A cost-recovery formula is also derived. Two methods for optimal design capacity are proposed that differ as to whether fare and frequency are exogenous or set conditional on capacity choice. Two numerical examples, one without and one with crowding, are presented to illustrate the theoretical results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".