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Record W4254317802 · doi:10.5383/jttm.01.02.004

https://iasks.org/articles/jttm-v01-i2-pp-27-34.pdf

2019· article· en· W4254317802 on OpenAlexvenueno aff
Hagos Gebremedhin Kibret

Bibliographic record

VenueInternational Journal of Traffic and Transportation Management · 2019
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsnot available
FundersWolkite UniversityUniversiteit Hasselt
KeywordsPedestrianVendorTransport engineeringPreferred walking speedComputer scienceSimulationEngineeringPhysical medicine and rehabilitationMedicineBusinessMarketing

Abstract

fetched live from OpenAlex

Street vendors use sidewalks to display goods and services. The reduction of sidewalk space by sidewalk vending activity forces pedestrians to take evasive action by changing walking speed and/or direction. Based on previous qualitative studies pedestrian evasive movements are related to pedestrian level of service and sharing carriageways. The aim of this paper was to investigate the effect of typical sidewalk vendor on average pedestrian walking speed and lateral position. The study used a field observation followed by a controlled walking experiment to study pedestrian behavior in the presence of typical sidewalk vendor. A univariate analysis of variance (ANOVA) of pedestrian trajectories, extracted from a walking experiment, showed that the average pedestrian lateral position and walking speed were significantly affected by the presence of a sidewalk vendor, pedestrian flow rate and the interaction effect of the two (p<0.05). The effect size also varied with the width of a vending stall and vendor's location relative to the pedestrian's desired trajectory. The results are consistent with previous observations and findings about pedestrian sidewalk behavior in the presence of sidewalk vendor. The findings may contribute in designing and or monitoring sidewalk vending activities.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.729

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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