Identification of Influencing Variables on Improving Resilience of High-Speed Railway System
Bibliographic record
Abstract
In recent years, frequent natural disasters have brought great challenges to the stable operation of the high-speed railway (HSR). Improving the resilience of the HSR system is an urgent problem to be solved. This study aims to explore how to improve the resilience of the HSR system. Based on an in-depth literature review and case study, 11 variables and 15 hypothetical paths were proposed. Then the questionnaires were distributed to professionals from academia and industry. A total of 270 valid responses were received. Finally, the structural equation model was used to evaluate these variables’ influence degree and their influence path. The results indicated that the infrastructure components (i.e., quality control and equipment operation and maintenance) play a positive role in the persistence ability. The organizational operation and maintenance components (i.e., organizational structure and organizational efficiency) promote the speed of function restoration. The interactive system components (i.e., technical system and system operation and maintenance) also have a positive effect on adaptability and transformability. The three components of HSR system play a positive role in different resilience attributes (i.e., persistence ability, function restoration, adaptability, and transformability), which further positively impacts the effectiveness of improving resilience. Based on the accepted hypothetical paths, five strategies for improving resilience were discussed, such as a certain degree redundancy of key organizations or members, encouragement of self-organized decision-making, and establishment of the “health records” of each HSR line. This study would enrich the theoretical system of resilience and help practitioners better understand the influencing variables and influencing paths of the resilience of the HSR system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".