Multiscenario-Based Train Headway Analysis under Virtual Coupling System
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Bibliographic record
Abstract
Chinese high-speed railway has implemented large-scale network operation with an urgent need for capacity improvement. The concept of virtual coupling seems to be a promising solution that provides a new operational scenario for high-speed railway, where trains are formed into a cooperative convoy and run synchronously with small train headways. The train-following principles under the virtual coupling signalling are quite different from those under conventional train control systems. Therefore, train headway analysis for different operational scenarios should be carried out to ensure railway safety and evaluate capacity benefits brought by virtual coupling. This paper proposes a potential virtual coupling architecture with reference to ETCS/ERTMS specifications. We compare blocking time models under different train control systems, and eight typical train-following scenarios are investigated for virtual coupling, including train arrival and departure cases. A detailed multiscenario-based train headway analysis is provided based on the microscopic infrastructure of the station and technological characteristics of virtual coupling. All computational outcomes are based on the train dynamic motion model. A comparative analysis of train headways under virtual coupling and CTCS-3 is provided in the case study. Results show that train headways can be substantially reduced under virtual coupling and are related to the station infrastructure layout.
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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 | 0.001 |
| 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 it