The modeling of urban rail transit emergency delay propagation scope under network operation mode
Bibliographic record
Abstract
Summary With the increase of the operation mileage of the urban rail transit and the extension of lines, the networked operation has become an inevitable trend of rail transit operation. Once an emergency takes place, it will cause delays of the large‐scale operation or even more serious mass safety incidents. First, the topology structure of urban rail transit network is analyzed. According to the static characteristics of the network clustering coefficient, degree, etc, a network model of directional empowerment network is constructed that can be used for analysis of the delay propagation scope. Second, the characteristics of the mass passenger flow distribution of rail transit in emergency is investigated and deeply explored in this paper. Based on this, the model of the delay spread and dissipation is constructed according to the emergency handling time and dissipation time of the passenger congestion. Finally, based on the cellular automaton model, the evolution rule for the delay spreading of the station state is established. The simulation results show that the established model has good reliability and accuracy, which can be adopted to predict the duration and spread of the delay in the emergency. This provides a basis for adjusting the transport organization plan and evacuating passenger flow in emergency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".