Interpretative Structural Modelling on Generation Mechanism of Train Operation Conflicts in High Speed Railway
Bibliographic record
Abstract
To detect and mitigate the operation conflicts of high-speed trains, it is critical to clarify the generation mechanism of train operation conflicts (TOCs) in high speed railway (HSR). Taking train delays as the precondition of the TOCs, this paper sorts out the main causes of the TOCs into four aspects, namely, equipment facilities, human behaviour, external environment, and organization management, and obtains a total of direct and indirect impact factors for HSR TOCs. Then, the interpretative structural modelling (ISM) was adopted to construct the adjacency matrix between these factors, and calculate the reachability matrix. On this basis, a directed hierarchical graph was plotted for the TOC causes based on the hierarchical relationship between the factors. The results show that HSR TOCs are directly caused by equipment facilities, transport organizations, and dispatcher professionality, and indirectly induced by natural environment, equipment operating environment, unexpected passenger flow, as well as the psychological quality, educational level, and years of service of dispatchers; in addition, the working environment, and the management of equipment and dispatchers are the deep-seated reasons for the TOCs. The research results provide new insights into the intelligent dispatching command of the HSR.
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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".