Optimal Operation Scheme with Short-Turn, Express, and Local Services in an Urban Rail Transit Line
Bibliographic record
Abstract
Flexible railway operation modes combining different operation strategies, such as short-turn, express, and local services, can significantly reduce operator and user costs and increase the efficiency and attractiveness of rail transit services. It is therefore necessary to develop optimization models to find optimal combinations of operation strategies for urban rail transit lines. In this paper, a model is proposed for solving the urban rail transit operation scheme problem. The model considers short-turn, express, and local services with the aim of minimizing the operator’s and users’ costs. The problem is first decomposed into two subproblems: the service route design problem and the passenger assignment problem. Then, a mixed-integer nonlinear program (MINLP) model is formulated, and linearization techniques are utilized to transform the MINLP model into a mixed-integer linear programming (MILP) model that can be easily solved by commercial optimization solvers. To accelerate the solution process, a heuristic search algorithm is proposed to obtain (nearly) optimal solutions based on the characteristics of the model. The two subproblems are solved iteratively to improve the quality of solutions. A real-life case study in Chengdu, China, is performed to demonstrate the effectiveness and efficiency of the proposed model and algorithm.
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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 | 0.002 |
| 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".