Pricing for Railway Group Tickets in Revenue Management Increasing Revenue and Attracting New Users
Bibliographic record
Abstract
The purpose of launching railway group tickets for railway enterprises is twofold: (1) increase revenue; (2) attract new users to travel by railway. In order to study how to achieve the above goals through price strategies for group tickets, this paper proposes an optimization approach for railway group ticket pricing in a scenario of multitrains. First, based on the consistent preference of passengers for group tickets, we model the decision‐making process of existing passengers purchasing group tickets and calculate the required quantitative boundary of existing passengers for selling out group tickets in order of priority. Then, under the constraints of stochastic demand and shared seat quota between group tickets and individual tickets, a multiobjective nonlinear optimization model with the objectives of maximizing both total expected revenue and expected sales of new users is constructed and solved. The analysis results reveal that there is no unique optimal solution simultaneously maximizing the two objectives. Increasing expected revenue will sacrifice the goal of attracting more incremental passengers to take trains. Limited by the fixed seat allocation, a scientific moderate discount scheme on group tickets can increase the total expected revenue. At this time, selling both group tickets and individual tickets yields higher revenue than only selling individual tickets, thus verifying the rationality of the mixed sales strategy of group tickets and individual tickets. Furthermore, we find an indicator named “elasticity of existing passengers” that has a critical impact on the expected revenue. Railway enterprises should take measures to incentivize the marketing enthusiasm of third‐party sales agencies to minimize the elasticity of existing passengers to achieve greater revenue.
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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".