Method for Determining the Valid Travel Route of Railways Based on Generalised Cost under the Syncretic Railway Network
Bibliographic record
Abstract
Travel route options for passengers can provide data support for railway line planning, passenger flow organisation, and train operation establishment. A critical review of the literature indicates that previous studies mainly focused on choices offered by a single railway network path without much consideration of China’s normal-speed and high-speed integrated railway network and the effect of train timetable on passengers’ travel choice. In this study, a method based on generalised cost is proposed to discover the valid routes of passenger travel in the integrated network of China’s normal-speed and high-speed railways. After quantifying the effects of train fare, travel time, transfer, travel convenience, comfort, and other factors on the generalised expenses of passengers, this study presents a generalised cost determination method when individuals select an option from different seats of different trains of specific railway transport products. Theoretically, the valid routes considering the train schedule is defined, and a valid route search algorithm is designed using the deep traversal idea in a new valid route searching network. Considering the Lanzhou-Beijing passenger travel routes as an example, this study verifies the practicability of the generalised cost calculation method, as well as that of the valid routes search method.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".