Improving the Planning of Future Track Interventions Using Digital Tools
Bibliographic record
Abstract
Abstract This paper proposes a methodology to capitalise on the recent advances in technology to efficiently estimate the required condition-related track interventions, possession times and their expected costs for a railway network early in the intervention planning process. Having such estimates not only helps track managers effectively plan and allocate resources, but it also enhances the communication between different stakeholders within the intervention planning process, e.g., asset managers, line planners, capacity managers, and network developers. The methodology uses data of different levels of detail, probabilistic discrete state modelling of the condition of components, and component-level intervention strategies. It also uses fault trees to connect potential losses in service with the likelihood of corrective interventions that may occur due to sudden events as a function of the condition of the components. The methodology is used to estimate the required condition-related interventions, possession times and expected costs for a 25 km railway network in Switzerland. The results indicate that the methodology has the potential to help track managers early in the intervention planning process. Once implemented in a digital environment, the methodology will lead to improvements in the efficiency of the planning process, improvement in the timing of preventive interventions and the reduction in corrective intervention costs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".