Identification and Evaluation of Urban Rail Transit Operation Risk Factors Based on Entropy-AHP Hybrid Constrained DEA Method
Bibliographic record
Abstract
The whole society is increasingly aware of the importance of urban rail transit (URT) safety and risk management. In order to correctly identify and evaluate the risk factors of URT operation and then classify and take differential measures to deal with the risk factors, the risk sources of URT accidents were analyzed by fault tree analysis and the URT operation risk evaluation index system concerning 8 categories and 40 risk sources were constructed. The DEA (data envelopment analysis) method with entropy-AHP hybrid constraint was established, and the probability and consequences of URT operation risk were taken as decision variables to evaluate and rank various risk factors. The results show that decision-making units (DMU) 1and 2， namely, vehicle system failure and signal communication system failure, are at the largest Pareto risk units, the probability of risk can be reduced by 50.08% and 41.7%, and the risk consequences can be reduced by 35.57% and 46.83%, respectively. Staff factors and environment and management factors are the other two units closest to the maximum risk surface. A risk factor distribution matrix was established, and 40 risk factors are divided into risk factors, leverage risk factors, conventional risk factors, and influential risk factors, which provide a basis for the classification and policy of rail transit risk management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".