A multiobjective planning model for intercity train seat allocation
Bibliographic record
Abstract
Abstract This paper presents a multiobjective planning model for generating optimal train seat allocation plans on an intercity rail line serving passengers with many‐to‐many origin‐destination pairs. Two planning objectives of the model are to maximise the operator's total passenger revenue and to minimise the passenger's total discomfort level. For a given set of travel demand, train capacity, and train stop‐schedules, the model is solved by fuzzy mathematical programming to generate a best‐compromise train seat allocation plan. The plan determines how many reserved and non‐reserved seats are to be allocated at each origin station for all subsequent destination stations on each train run operated within a specified operating period. An empirical study on the to‐be‐built Taiwan's high‐speed rail system is conducted to demonstrate the effectiveness of the model. The model can be used for any setting of travel demand and stop‐schedules with various train seating capacities.
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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 | 0.001 |
| 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".