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Record W1908035642 · doi:10.1002/atr.1223

Personal space, evasive movement and pedestrian level of service

2013· article· en· W1908035642 on OpenAlexvenueno aff
Sangyoup Kim, Jaisung Choi, Sungkyu Kim, Richard Tay

Bibliographic record

VenueJournal of Advanced Transportation · 2013
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPedestrianLevel of serviceService (business)Space (punctuation)Transport engineeringSample (material)Personal spacePerceptionMovement (music)Computer scienceEngineeringPsychologySocial psychologyBusiness

Abstract

fetched live from OpenAlex

SUMMARY This study proposed a behavioral theory‐based approach to better assess pedestrian levels of service for sidewalks, using the concepts of personal space and pedestrian evasive movements. Data from pedestrian interviews and video recordings at 28 commercial, residential, and leisure locations were used to analyze the effects of pedestrian movements on pedestrian perceived levels of service. With these results, a new measure of pedestrian level of service was developed. With the use of a separate validation sample, the revised levels of service obtained were found to be more consistent with the pedestrian perceived levels than the levels prescribed by the Korean Highway Capacity Manual. Hence, this research recommended the use of the revised measures for pedestrian level of service in the design and evaluation of pedestrian facilities. We also found that pedestrian evasive movements on sidewalks could better explain pedestrians' perception of the levels of service for a given facility. Moreover, pedestrian evasive movements were found to be sensitive to the width of the sidewalk and pedestrian volume. Copyright © 2013 John Wiley & Sons, Ltd.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.252

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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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 designObservational
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

Citations41
Published2013
Admission routes1
Has abstractyes

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