Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.749 | 0.771 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".