Integrated optimization of metro and bridging bus operation under metro disruption
Bibliographic record
Abstract
Existing studies on integrated optimization of metro and bridging bus operation under metro disruption often overlook the joint optimization of the train timetables and rolling stock circulations (i.e., the selections of turnaround stations, service cancellations, and service connections), as well as the routes and timetables of bridging buses. To address this gap, this study formulates the problem as a mixed-integer linear program. This program aims to minimize the total cost, including the cost of train timetable deviation before and after adjustment, the cost of the number of train service cancellations, the cost of the headway deviations of train services, the cost of the average passenger delay, and the cost of bridging bus operation time. An enhanced Adaptive Large Neighbor Search Algorithm is developed to solve the model. Three customized destroy operators and repair operators are designed to rapidly find high-quality solutions. Experiment results on Metro Line 1 in Urumqi demonstrate that the proposed model achieves 13.83% improvement compared with the model separately optimizes metro and bridging bus operation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".