The potential cost from passengers and how it impacts railway maintenance and renewal decisions
Bibliographic record
Abstract
To plan Maintenance and Renewals (M&R) for the heavy railway lines, scheduling work possession time and deciding the closure of railway line are quite challenging for Infrastructure Manager (IM) at tactical planning level. As usual, the direct costs such as the materials costs, man power price and machinery costs are the important factors for IM to evaluate all the alternative schedules. At the same time, the potential cost from passengers is also crucial to minimize the impacts to the society. A phase-based planning toolkit is developed to help IM to plan and compare project proposals from a wider cost scope, integrating the passenger loss and direct costs into the comparison at planning stage. Passenger loss is estimated basing on the potential delay time values. The case study shows the potential cost from passengers is one of the key factors impacting the rank of M&R options. It even dominates the overall cost comparison for the busiest railway stations. In such case, the track closure time has to be decided according to the passenger loss instead of the direct costs. Sometime the best proposal for society might be the most expensive solution for IM. Therefore the potential passenger loss is not something that can be ignored at planning stage.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".