Optimization of High-Speed Railway Line Planning Considering Extra-Long Distance Transportation
Bibliographic record
Abstract
Network line planning optimal considering extra-long distance transportation can adapt to high-speed railway (HSR) network operation environment and meet the demand of extra-long distance travel. First, the distance of 1500 km or more is defined as extra-long transportation distance based on market competitiveness. The difficulties of direct or transfer transportation for extra-long distance transportation are analyzed. Direct method of extra-long distance is determined according to direct passenger volume and the number of important nodes while transfer method is represented considering transfer times, organization forms, varieties of schemes, and comfort. Then, ideas of optimizing line plan based on riding scheme is put forward. Network optimizing line plan model with riding scheme is developed considering extra-long distance transportation. The model which minimizes traveling cost and train running expense optimizes train frequency and train stop plan with riding scheme. Last, the method and optimizing model is verified by the network which took Baoji–Lanzhou HSR as the core. The optimal result is a benefit to improve operation efficiency and market competitiveness of HSR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".