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Record W2348249203

Train Service Planning for Passenger Dedicated Railway Line Based on Analyzing Importance of Nodes

2010· article· en· W2348249203 on OpenAlexaff
Qing-Lan Zhu

Bibliographic record

VenueJournal of Beijing Jiaotong University · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsTransport Canada
Fundersnot available
KeywordsTrainAnalytic hierarchy processLine (geometry)Service (business)Transport engineeringDedicated lineComputer scienceHierarchyClass (philosophy)Operations researchLevel of serviceResource (disambiguation)Process (computing)EngineeringComputer networkArtificial intelligenceGeography
DOInot available

Abstract

fetched live from OpenAlex

Starting from the analysis of the economic and social attributes as well as railway resource allocation of the passenger dedicated line(PDL)covered nodes,this thesis introduces the concept of the importance of nodes.On the basis of analytical hierarchy process,the thesis studies the quantitative index of the nodes transport distribution capacity in the PDL network and calculates the evaluation indexes on the importance of those city nodes.Thus,in the light of the assessment results,the thesis sets up the three-level hierarchy in the importance of city nodes covered by the PDL.Based on that,the thesis proposes that the first-level nodes serving as the departure and arrival stations,the second-level nodes adopting the fluid and alternate stop stations,and the tertiary-level nodes takeing the form of all-stop for low-class trains so as to build a multi-objective programming model for the PDL train line planning.With the lingo 8.0 program,the train line planning optimizes the calculation of stop stations.This method has been applied in the train line planning of Wuhan-Guangzhou high-speed railway line and proved to be very effective in reducing the complexity of the train service planning problem.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.285
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2010
Admission routes1
Has abstractyes

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