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Record W2592370412 · doi:10.1155/2017/6381718

A Novel Space-Time-Speed Method for Increasing the Passing Capacity with Safety Guaranteed of Railway Station

2017· article· en· W2592370412 on OpenAlexvenueno aff
Junfeng Wang, Yang Yu, Renwei Kang, Jungang Wang

Bibliographic record

VenueJournal of Advanced Transportation · 2017
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsnot available
FundersState Key Laboratory of Rail Traffic Control and SafetyNational Natural Science Foundation of China
KeywordsVariable (mathematics)Process (computing)Computer scienceMode (computer interface)SimulationEngineeringReal-time computingMathematics

Abstract

fetched live from OpenAlex

A method for improving the passing capacity of a station without adding any track and equipment is proposed in this paper. In the process of handling train routes, by transforming the existing fixed train-approaching locking section into a variable mode, the route locking time is shortened and in-station resource consumption is reduced. This approach improves the capacity of the station. At the same time, delay of the train can be quickly returned to normal. A method of variable train-approaching locking section is discussed; a mathematical model for increasing station passing capacity is shown. Comparison between the impact of a variable train-approaching locking section and a fixed mode on the station passing capacity is shown.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations16
Published2017
Admission routes1
Has abstractyes

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