Impact Assessment of Interlocking Systems on Single-Track Railway Lines as a Measure Leading to Resilient Railway System
Bibliographic record
Abstract
Railway systems should be resilient to play a key role in creating sustainable development. Single-track railway lines are seen as potential bottlenecks due to limited capacity. More advanced railway interlocking systems (such as ETCS or satellite-based control systems) are being developed. On the other hand, the installation of these interlocking systems is a complex and time-consuming and costly task. For this reason, it is necessary to recognize the impact of potentially installed system with capacity, stability of timetable, quality, and other associated effects. The assessment is based on a set of simulation experiments using stochastic microscopic simulation model in the OpenTrack software tool. The focus is on railway operation with automatic block and automatic line blocking systems. If these two systems will have positive capacity effects, it is a basic presumption also for systems such as moving block (e.g., ETCS L3) to be effective. Research has shown that the significance of such measures can be best supported by linking to a matching timetable concept that will make full use of the benefits offered by these interlocking systems. The results reached in this research should be potentially applied, for example, by prioritizing of single-track railway lines for possible installation of such interlocking system. It can be achieved based on the capacity and operational effects examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".