Optimization of Skip‐Stop Train Schedule in Urban Rail Transit Under Virtual Coupling
Bibliographic record
Abstract
Virtual coupling technology enables decoupling and coupling operations of trains more flexible, and the tracking interval between trains is also shortened. This paper analyzes the advantages of virtual coupling technology in the application of skip‐stop operation, and operation strategies based on real‐time train coupling and decoupling are proposed. With the goal of minimizing the total travel time of passengers, an optimization model for skip‐stop timetable under virtual coupling technology is established, which limits the virtual operation location, the number of virtual coupling and decoupling, and the relationship between virtual operation and arrival and departure status of trains. At the same time, an adaptive large‐scale neighborhood search algorithm was designed for model solving. A case study of certain urban rail transit line R, which is a skip‐stop‐operated urban rail transit line, has been carried out. The model and algorithm were validated using real data from the morning peak hour, and the results show that the application of virtual coupling technology can effectively reduce the total travel time of passengers. Under the conditions of this case study, virtual coupling technology can improve service quality. In actual operation, under suitable passenger flow and line conditions, the reasonable use of virtual coupling technology to improve the skip‐stop operation mode can improve the service quality and operational efficiency of urban rail transit.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".