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Record W3121672237 · doi:10.1155/2021/8862893

Discrete Event Simulation of Bus Terminals: A Modular Approach with a High Spatial Resolution

2021· article· en· W3121672237 on OpenAlexvenueno aff
Therése Lindberg, Fredrik Johansson, Anders Peterson, Andreas Tapani

Bibliographic record

VenueJournal of Advanced Transportation · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
FundersK2
KeywordsModular designTerminal (telecommunication)QueuePing (video games)Event (particle physics)Discrete event simulationComputer sciencePublic transportSimulationEngineeringTransport engineeringComputer networkOperating system

Abstract

fetched live from OpenAlex

Interchange stations with their many connecting modes and lines are central for a high-quality public transport system. Bus access at the station needs to operate reliably and efficiently in order to prevent congestion and queues. To this end, a conceptual simulation model for evaluation of bus terminal operations is presented in this paper. It is based on discrete event simulation and able to describe the detailed movements and interactions that occur between vehicles at larger terminals. The model has a modular approach, where common spatial sections at terminals are represented by modules that can be easily combined into many different terminal layouts. An implementation of the model is presented and, as a first sensitivity test, applied in a numerical experiment representing Norrköping interchange station in Sweden. The results indicate that the model can be a useful tool in planning processes.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.284
Teacher spread0.272 · 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
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

Citations0
Published2021
Admission routes1
Has abstractyes

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