Optimization Train Stop Planning for High‐Speed Railway Considering Flexible Ticket Pricing and Elastic Demand
Bibliographic record
Abstract
Currently, the optimization of train stop planning of high‐speed railways does not adequately account for how ticket adjustment and train departure time affect passenger expectations. To better align supply and demand dynamics and maximize passenger utility, it is essential to optimize train stop planning, ticket pricing strategies, and passenger flow allocation from a system‐wide perspective. The paper proposes a synergistic optimization model for train stop planning, ticket pricing, and passenger flow allocation of high‐speed railway. The optimization model aims to maximize the operational revenues of the transportation enterprise while minimizing the total travel costs of passengers. The number of train stops, range of ticket price fluctuations, transportation capacity, and price response function are taken into consideration. A double‐layer simulated annealing algorithm is designed to solve the model. Finally, a real case based on the Hohhot East–Beijing North high‐speed railway in China verifies the correctness and validity of the model. A comparative analysis of the operational benefits of the railroad transportation enterprises and the travel utility of the passengers under the original plan and the synergistic optimization plan is carried out to verify the validity of the model and the algorithm. The results show that the method proposed in this paper can improve the operational efficiency of transportation enterprises by 18.54% and reduce the passenger travel time costs by 12.13% without increasing the number of trains and the number of stops. The optimized plan can reduce train operation costs, meet passenger flow demand, and improve the operational efficiency of transportation enterprises and the travel utility of passengers.
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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".