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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".