Line Planning under the Operation Mode of Line Sharing between Metro and Suburban Railway
Bibliographic record
Abstract
Due to high transport efficiency, reduced transfer time, and various other advantages, the joint operation of different rail transit systems emerges as the optimal choice for rail transit systems. This article mainly studies the line-planning problem under the line-sharing operation mode between metro and suburban railway. First, a complex multiobjective programming model is established to maximize the net profit of two operating companies and the time savings of passengers. The constraints of this model encompass passenger flow, available vehicles, line carrying capacity, station capacity, cross-line configuration, departure frequency, and variable value range. Second, the linear weighted sum method is introduced to consolidate three objective functions into a single one, while utilizing the improved artificial bee colony (IABC) algorithm to address the line-planning problem. Besides, the traditional artificial bee colony (TABC) algorithm and the simulated annealing (SA) algorithm are provided as comparison groups to solve the same numerical example problem. The results demonstrate significant reductions in travel time by adopting the line-sharing operation mode. In addition, the IABC algorithm exhibits better solution quality and higher efficiency than both the TABC and SA algorithms. The proposed method proves to be valuable in formulating and optimizing the line plan.
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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 | 0.000 |
| 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".