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Record W2598010875 · doi:10.5278/ojs.td.v1i1.5675

The potential cost from passengers and how it impacts railway maintenance and renewal decisions

2020· article· en· W2598010875 on OpenAlexaff
Rui Li, Alex Landex, Otto Anker Nielsen

Bibliographic record

VenueTechnical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · 2020
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsTransport Canada
Fundersnot available
KeywordsTransport engineeringBusinessOperations researchEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.178
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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