Automatic Data for Applied Railway Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2009, London Overground management implemented a new tactical plan for a.m. and p.m. peak service on the North London Line (NLL). This paper documents that tactical planning intervention and evaluates its outcomes in terms of certain aspects of service delivery (the operator's perspective on system performance) and service quality (the passenger's perspective). Analyses of service delivery and quality and of passenger demand contributed to the development, proposal, and implementation of the new tactical plan. NLL trains were delayed routinely en route, with excessive dwell time a major cause. Near-random passenger incidence behavior suggested that an even headway service for NLL might have been more appropriate. The confluence of these analyses was confirmed by the corresponding excess journey time results. On the basis of longitudinal analysis, an evaluation showed that on-time performance increased substantially and observed journey time decreased with the introduction of the new plan. Overall, the effects of this implementation appeared to have been positive on balance. This case study thus demonstrated the applicability of automatic data generally, and certain measures and techniques in the London Overground specifically, to support the tactical planning of an urban railway.
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.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.006 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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