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Record W4220742330 · doi:10.1155/2022/8895935

Research on Coordinated Passenger Inflow Control for the Urban Rail Transit Network Based on the Station-to-Line Spatial-Temporal Relationship

2022· article· en· W4220742330 on OpenAlexvenueno aff
Ruixia Yang, Weiteng Zhou, Baoming Han, Dewei Li, Bin Zheng, Fangling Wang

Bibliographic record

VenueJournal of Advanced Transportation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesBeijing Municipal Natural Science FoundationBeijing Jiaotong UniversityNational Natural Science Foundation of China
KeywordsInflowBeijingUrban rail transitVolume (thermodynamics)Transport engineeringControl (management)Control volumeFlow (mathematics)Level of serviceLine (geometry)Computer scienceEngineeringMeteorology

Abstract

fetched live from OpenAlex

This study proposes a coordinated inflow control organization for the urban rail transit network to improve train capacity utilization and reduce inbound delay rate, considering existing operational requirements and station-to-line spatial-temporal relationship of passenger flow. The coordinated passenger flow control model is proposed with the objective to maximize the inbound flow rate and the train full load rate. A station-to-line spatial-temporal correlation formula is constructed to characterize the relationship between station inbound passenger volume and section passenger volume. A two-stage approach is employed to solve this passenger flow control problem. The proposed model and solution strategy are evaluated on a well-known Beijing network with 10 operating lines. The refined inflow control scheme is displayed with the accurate inbound volume at each station during each time period. The comparison between the proposed control strategy and the current control strategy shows that the former can effectively improve the service level and capacity utilization.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.343
Teacher spread0.296 · 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 designSimulation or modeling
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

Citations4
Published2022
Admission routes1
Has abstractyes

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