Together Apart: Increased Crowd Heterogeneity Influences Crowd Dynamics and Interpersonal Distance at Bottlenecks
Bibliographic record
Abstract
Abstract Despite considerable research efforts, most controlled empirical studies on crowd movement usually rely on homogeneous crowds, i.e., research participants are typically young adults without disabilities. Consequently, little is known about pedestrian movement in more diverse and heterogeneous crowd conditions, e.g., when persons with reduced mobility are present. This gap may be particularly relevant at bottlenecks, along the path of a moving crowd that limit the capacity of pedestrian flow. Here, we present results from 12 studies in which participants (total N = 252) with and without visible disabilities moved together in a crowd. In each study, groups of participants walked together in a hallway with a bottleneck at the end. The point of speed adoption, distances between neighbours, and behavioural activities were analysed. We found (1) that participants with disabilities reduced their speed further away from the bottleneck than participants without disabilities; (2) participants without disabilities stayed closer to neighbors with disabilities than to neighbors without disabilities; and (3) participants interacted and communicated with each other to organise in front of the bottleneck. These results underline the importance of studying representative and heterogeneous samples in crowd dynamics. We also argue that more interdisciplinary research is needed to better understand the dynamics of interactions between neighbors in a crowd. A more nuanced understanding of pedestrian dynamics holds the promise of improving the validity of simulation tools such as movement and evacuation models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".