Collaborative Optimal Train Carriage Flexible Release Strategy and Passenger Flow Control Strategy for the Metro System
Bibliographic record
Abstract
In the oversaturated metro system, the mismatch between supply and demand leads to unequal allocation of train capacity at different stations, resulting in a transportation inequity issue. This paper proposes a collaborative optimization method to use train carriage flexible release strategy and passenger flow control strategy, which is described as a mixed‐integer nonlinear programming (MINLP) model considering the trade‐off between equity and efficiency. To solve this model, it is reformulated into a mixed‐integer linear programming (MILP) model, which is solved by the GUROBI solver. An efficient variable neighborhood search algorithm is then proposed to find a high‐quality solution to the proposed problem. Finally, two sets of numerical experiments, including a small‐scale case and a real‐world case of Chengdu metro system, are conducted to verify the proposed model. The experimental results show that the train release scheme and passenger flow control scheme generated by our proposed method can perform well on the trade‐off between equity and efficiency.
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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 | 0.000 | 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 | 0.000 |
| 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".