Towards a Personalized Seat Selection System of High-Speed Rail Based on Feasibility and Desirability
Bibliographic record
Abstract
To conduct this research, we identified the current high-speed rail (HSR) market and passenger experience satisfaction with HSR. Then, we developed a personalized seat selection system that could assign passengers to different seats based on their own preferences by collecting their basic information. In the current HSR field, the existing research mainly focuses on improving hardware, such as seat comfort, high-speed rail traveling speed, and high-speed rail traveling smoothness. It does not consider travelers' environmental preferences and individual needs to a great extent. After several weeks of market research through the questionnaire and 4P model study, we concluded that the vast majority of passengers are dissatisfied with the current travel environment full of interruptions and are really willing to use a system that will improve their travel comfort. Therefore, the system will be added to a few major OTA platforms as a plug-in in order to reach a large number of potential customers. After that, we designed many features for our system based on the needs and interests of our target customers, as shown in the survey results, to confirm the desirability and feasibility of our product.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".