The Effect of Wheelchair Users on the Egress Time of Pedestrian Crowds: A Systematic Literature Review and Meta-analysis
Bibliographic record
Abstract
Abstract Egress time, or how long it takes a pedestrian crowd to pass through a bottleneck during egress, is a crucial metric for safety and capacity considerations. It has been suggested that heterogeneity in the composition of pedestrian crowds - such as variability in mobility, age, or the presence of social groups - could affect egress times. However, only a few empirical studies have addressed this issue. To solidify insights from the existing empirical evidence, we present a systematic literature review and meta-analysis to quantify if the presence of wheelchair users in pedestrian crowds increases egress times. We identified nine studies, all based on controlled experiments, that used a comparable layout in which groups of participants had to move through a bottleneck and compared conditions with and without wheelchair users present. The meta-analysis confirmed the findings from the individual studies. The difference in egress time between conditions with wheelchair users present and those without was close to three standard deviations, indicating a strong effect. We found no evidence for publication bias, such as the under-reporting of non-significant findings. Our work presents a quantitative basis for adjusting expected egress times depending on occupant characteristics. It suggests that the behavioural consequences of crowd heterogeneity are safety relevant and require further investigation.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".