Simultaneous Optimization of Train Timetabling and Platforming Problems for High-Speed Multiline Railway Network
Bibliographic record
Abstract
The optimization problems of train timetabling and platforming are two crucial problems in high-speed railway operation; these problems are typically considered sequentially and independently. With the construction of high-speed railways, an increasing number of interactions between trains on multiple lines have led to resource assignment difficulties at hub stations. To coordinate station resources for multiline train timetables, this study fully considered the resources of track segments, station throat areas, and platforms to design a three-part space-time (TPST) framework from a mesoscopic perspective to generate a train timetable and station track assignment simultaneously. A 0-1 integer programming model is proposed, whose objective is to minimize the total weighted train running costs. The construction of a set of incompatible vertexes and links facilitates the expression of difficult constraints. Finally, example results verify the validity and practicability of our proposed method, which can generate conflict-free train timetables with a station track allocation plan for multiple railway lines at the same time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".