Resilience Assessment and Recovery Strategy for High‐Speed Railway Networks Considering Spatiotemporal Dynamic Characteristics
Bibliographic record
Abstract
The service link in a high‐speed railway (HSR) network has an evident temporal attribute, and conventional research methods ignore the importance of temporal information in resilience assessments. Hence, in this study, an HSR service network model based on a temporal network framework is constructed, and an HSR service network resilience assessment method considering spatiotemporal dynamic characteristics is proposed. Considering the heterogeneity of train flow in different time periods and taking the time cost of the shortest temporal path as the network performance measure, a resilience assessment model is established based on the HSR temporal service network, and an algorithm for solving the network performance is designed. Taking China’s HSR network as a case study, the research results showed that the optimal recovery strategy exhibited a higher resilience value than four other recovery strategies. In different spatiotemporal dimensions, the impact of disturbance events on network resilience is different, and the corresponding optimal restoration sequence is also different, making the resilience of the HSR network to exhibit evident differences in the spatiotemporal distribution. In addition, an increase in the number of repair resources is not proportional to the improvement in network resilience. The railway emergency department should comprehensively consider the spatiotemporal characteristics of the disturbance distribution and reasonably determine the restoration sequence and the number of repair resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".