A Regret Theory-Based Decision-Making Method for Urban Rail Transit in Emergency Response of Rainstorm Disaster
Bibliographic record
Abstract
The decision-making for urban rail transit emergency events takes an important role in both reducing the losses caused by disasters and ensuring the safety of passengers. For the rainstorm emergency decision-making without certain scenario prediction information, considering the characteristic that the predisaster prevention measures will influence the effect of in-process countermeasures, this paper aimed to analyze the whole process scenarios for the occurrence, evolution, and development of rainstorm disaster in urban rail transit by considering the regret aversion of the decision makers. An emergency decision-making method for the beforehand-ongoing two stages rainstorm emergencies was developed to assess the emergency decision-making of urban rail transportation in different rainfall flood scenarios. Besides, the utilities and application costs of the emergency plans are also considered when defining the optimal emergency decision-making. This paper purposes the emergency decision-making model based on regret theory to define the optimal predisaster prevention method and ongoing responding measure for different disaster scenarios. Taking the Tianjin rail transportation as an example, this paper defines the optimal emergency decision-making to respond typhoon “Lekima.” The results show that if this method can be implemented in the rail transportation rainstorm disaster emergency responding and relevant disaster prevention management, then the reliability and risk responding capability of public transportation service can both be improved.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".