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Record W4312888502 · doi:10.1109/access.2022.3220321

Pedestrian Traffic Characterization Based on Pedestrian Response

2022· article· en· W4312888502 on OpenAlexaff
Shamsul Islam, T. Aaron Gulliver, Khurram Shehzad Khattak, Waheed Imran

Bibliographic record

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPedestrianSimulationComputer scienceEmergency responseTransport engineeringEngineeringMedicine

Abstract

fetched live from OpenAlex

Pedestrian behavior is studied during normal and emergency evacuation of two groups of students. One group consists of grade nine high school students while the other group includes diploma (college) students. The evacuation time, number of steps, step frequency, and velocity are observed. It is found that the number of steps, step frequency, and velocity are larger during an emergency evacuation. These results are used to develop a macroscopic pedestrian response model. This model is compared with the Lighthill, Whitham, and Richards (LWR) model for pedestrian traffic using the First Order Centered (FORCE) scheme. Pedestrian parameters from the experiments are used for model evaluation. The direction of pedestrian movement is changed multiple times to observe pedestrian alignment behavior. It has been found that the proposed model performs more realistically than the LWR model.

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 score0.018
Threshold uncertainty score0.575

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)0.0010.000

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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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