Identifying Station Importance in Urban Rail Transit Networks Using a Combination of Centrality and Time Reliability Measures: A Case Study in Beijing, China
Bibliographic record
Abstract
Abstract Time reliability (TR) is a critical factor that affects the efficiency and service quality of the urban rail transit network (URTN). However, previous studies have not incorporated TR into the evaluation of URTN station importance, focusing instead on basic centrality measures. Therefore, this paper proposes a new metric of station-based TR for evaluating and ranking URTN station importance. The new metric in combination with traditional centrality measures was used by the weighted Technique for Order of Preference by Similarity to Ideal Solution (weighted TOPSIS) to identify the combined significance level of individual URTN station importance and rank them accordingly. To investigate the performance of this method, we exploit deliberate attacks on the top-ranked stations through different methods. A case study of Beijing’s URTN during the morning peak hour showed that the proposed method is generally a better indicator for identifying station importance in maintaining network connectivity. The case study also demonstrated the feasibility and validity of the model. This study can provide recommendations for the planning and operation of rail transit systems and can inform the effective design of station protection strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".