Seat inventory control for intercity passenger rail: how it works with customer satisfaction
Bibliographic record
Abstract
Many intercity railway companies employ “first-come-first-serve (FCFS)” concept in reservation system. The “FCFS” concept seems fair to passengers but not effective in overall passenger load and revenue management. For example, long distance passengers cannot purchase tickets because seats are not available in some sections, during the wanted origin and destination (O-D), which are purchased earlier by short distance passengers. Moreover, those empty seats are waste of opportunity at the departure. To eliminate the bottleneck, we consider using seat inventory control in high speed rail. Seat inventory control is a concept in revenue management, or yield management, for example keeping some seats for long distance passengers who may come later, instead of selling to earlier-comer short haul O-D passenger. The network can earn higher revenue and improve serviceability (in passenger-km) and overall passenger load by maximizing the utility of existing facilities (see Cross(1997), Daudel and Vialle(1994), McGill and Van Ryzin(1999), and Talluri and Van Ryzin(2004) for overview on revenue management).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".