Profit Maximization Model with Fare Structures and Subsidy Constraints for Urban Rail Transit
Bibliographic record
Abstract
This paper analyzes government subsidies based on the service design (i.e., headway) and fare structures of an urban rail transit system while considering necessary financial support from the government. To capture the interactions among the operator performance, government subsidies, and passengers in an urban rail transit system, a profit maximization model with nonnegative profit constraint is formulated to determine the optimal fare and headway solutions. Then, the social welfare that results from the operator profit maximization model is analyzed. Finally, a numerical example from Changsha, China, is employed to verify the feasibility of the proposed model. The major results consist of optimized solutions for decision variables, i.e., the fares and train headways, as well as subsidies to the operator. The fare elasticity factor under two fare structures significantly affects fares and demand. As the fare elasticity factor increases, the social welfare gradually decreases and a deficit occurs at low fares and demand, while subsidies rise from 0 to ¥24658.00 and ¥38089.16 under the flat fare and distance-based fare structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".