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Record W2167844517 · doi:10.1002/atr.5670370104

Development of an object‐oriented pedestrian traffic flow simulation environment for transport terminal planning

2003· article· en· W2167844517 on OpenAlexvenueno aff
Dimosthenis Anagnostopoulos, Matthew G. Karlaftis, Μάρα Νικολαϊδου

Bibliographic record

VenueJournal of Advanced Transportation · 2003
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPedestrianTerminal (telecommunication)Traffic flow (computer networking)Component (thermodynamics)Representation (politics)Traffic simulationComputer scienceObject-oriented programmingEmergency evacuationSimulationService (business)Transport engineeringObject (grammar)Level of serviceSystems engineeringEngineeringIntersection (aeronautics)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract In recent years, simulation has become an essential tool for planning, designing, and managing terminal operations. Simulation modeling of various aspects related to the dynamic nature of the pedestrian/passenger behavior within a transport terminal can effectively assist in the analysis, evaluation, emergency response planning, and decision support phases of the terminal's operation. In this paper, the development of an object‐oriented environment that enables both the graphical description and simulation of pedestrian traffic flows at the microscopic level is discussed. The simulation environment provides generation capabilities so that a terminal station model is directly formed on the basis of preconstructed component models. Essential experimentation capabilities, including graphical representation, are provided for pedestrian‐oriented and system‐oriented measures of interest with explicit emphasis on the level of service. A case study for a 4–level station in the new Athens subway system is also used to demonstrate the potential and functionality of the simulation environment.

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 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: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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 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

Citations6
Published2003
Admission routes1
Has abstractyes

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