Flexible Train Composition Mode–Based Rolling Stock Circulation Planning Problem for Regional Rapid Rail Transit
Bibliographic record
Abstract
Regional rapid rail transit is an emerging rail transit system in China in recent years, with the same level of service frequency and longer station spacing as metro. The traditional fixed train composition mode has weak adaptability to its unbalanced transport demand in time and space, leading to high rolling stock traveling kilometers and operation costs. As a novel operation strategy, the flexible train composition mode can make up for this shortcoming, but the matched rolling stock circulation planning is a complex optimization problem. This paper proposes an operation mechanism of the rolling stock circulation plan under flexible train composition mode with multiple coupling/decoupling operation sites for regional rapid rail transit, where trains can change compositions at both terminal and intermediate stations. A mixed‐integer nonlinear programming (MINLP) model is constructed for rolling stock circulation planning based on the proposed mechanism. The optimization objective is to minimize the total operation costs of train services, depot entry/exit processes, and coupling/decoupling activities at terminal and intermediate stations. The model is then reformulated to an equivalent mixed‐integer linear programming (MILP) model, which can be solved by the CPLEX solver. A numerical experiment based on the real‐world data from a regional rapid rail transit line in China is designed to verify the effectiveness of the model and solution approaches. The results show that the obtained rolling stock circulation plan effectively reduces the rolling stock traveling kilometers and operation costs with the pregiven timetable. The methods in this paper provide dispatchers with more options to better match the transport demand of regional rapid rail transit.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".