Novel approach to optimizing express–local metro train timetable considering multiple train stopping patterns
Bibliographic record
Abstract
A traditional hybrid operational mode of a metro system usually combines the express and local trains. The stopping patterns of express trains are generally assumed to be the same. To provide a diverse service, this paper aims to explore the train timetable optimization method when the express trains have different stopping patterns. First, all the trains are categorized into five cases based on the type of train and the order of departure. A passenger-oriented timetabling model for reducing passenger waiting time is established, which can be solved by the Interior Point Method. Numerical examples from Guangzhou Metro Network are conducted with six scenarios, symbolizing different operation strategies. The proposed model was validated through numerical experiments conducted on a Nanjing metro line. The results indicate that the proposed model can efficiently reschedule the timetable in express/local mode with multiple stopping patterns and reduce the passenger waiting time, the most considerable reduction of which comes up to 16.8%, along with a reduction in their extra waiting time of up to 17.3%.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".