Calculation Method for Load Capacity of Urban Rail Transit Station considering Cascading Failure
Bibliographic record
Abstract
The load capacity of urban rail transit station is of great significance to provide reference in station design and operation management. However, it is difficult to carry out quantitative calculation quickly and accurately due to the complex interaction among passenger behaviors, facility layout, and the limit capacity of single facility. In this paper, the association network of facilities is set up based on the analysis of passenger service chain in station. Then the concept of cascading failure is introduced to the dynamic calculation model of load capacity, which is established on the user-equilibrium allocation model. The solution algorithm is optimized with node attack strategy of complex network to effectively reduce the computational complexity. Finally, a case study of Lujiabang Road Station in Shanghai is carried out and compared with the simulation results of StaPass, verifying the feasibility of this approach. The proposed method can not only search for the bottleneck of capacity, but also help to trace the loading variation of facilities network in different scenarios, providing theoretical supports on passenger flow organization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".