Stochastic modelling of train delays and delay propagation in stations
Why this work is in the frame
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Bibliographic record
Abstract
A trade-off exists between efficiently utilizing the capacity of railway networks and improving the reliability and punctuality of train operations. This dissertation presents a new analytical probability model based on blocking time theory which estimates the knock-on delays of trains caused by route conflicts and late transfer connections in stations. The model estimates the propagation of train delays with a higher accuracy than existing analytical models by taking into account the interdependences of the arrival and departure times of different train lines and the dependences of the dwell times of trains on arrival delays. A detailed statistical analysis of real-world traffic data reveals that the variations of train events and process times can be well approximated by either the lognormal distribution or the Weibull distribution. Given the mean and standard deviation of input delays at the boundary of a station and those of primary delays within the area, the knock-on and exit delay distributions are estimated by means of the stochastic models. Consequently, the maximal traffic capacity utilization of complex stations and interlocking areas can be estimated according to a desired level of train punctuality. The research results support railway infrastructure managers, timetable designers, and train operators in optimizing the network capacity utilization and train scheduling.
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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 | 0.001 | 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