Bilevel Programming for Evaluating Revenue Strategy of Railway Passenger Transport under Multimodal Market Competition
Bibliographic record
Abstract
A bilevel programming approach is used to study the strategies of increasing revenue of the railway agency running between Beijing and Tianjin, China. Bilevel programming approaches have been used in many studies to tackle a variety of transportation problems, but rarely for railway revenue strategy analysis. In this paper, the upper-level problem of the bilevel programming is to determine optimal pricing, speed, and level-of-service (LOS) strategy that maximizes the revenue of the railway agency. The lower-level problem describes passengers’ mode choice behavior under a transportation market with three competing modes: bus, rail, and car. The lower-level problem is to minimize the traveler's cost in terms of money, time, and other related factors such as comfort and safety. A generalized cost function, considering these factors together with a logit model, is used to simulate travelers’ mode choice behavior. Study results clearly show that the bilevel programming is appropriate for the problem studied here. The results indicate that, to increase its revenue, the railway agency should focus on not only the pricing but also travel time and LOS. A pricing breakpoint of about ¥31 (¥7 = U¥1) is found, as it results in the highest revenue for all traveling speeds and LOS. Further increase of the price leads to reduced revenue. A consistent revenue increase trend is observed for all higher traveling speeds and LOS, which emphasizes that the railway agency should pay attention to a combined revenue strategy.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".