Observed Behaviours in Simulated Close-Range Pedestrian Dynamics
Bibliographic record
Abstract
Crowd simulation can be a useful tool for predicting, analyzing, and planning mass-gathering events. The analysis of simulated crowds aims to extract observations to assess occupant interactions and potential crowd flow issues. This paper presents a continuous-space definition of Centroidal Particle Dynamics (CPD) crowd models, then proceeds to present behaviours observed in the simulated crowds. These include organized micro-grouping (flocking), uncooperative behaviors like passage blocking and collisions due to distracted pedestrians. It also briefly explores how spatial design choices could positively impact pedestrian flow. The observations might be of interest to designers of urban and architectural spaces who are looking to improve pedestrian or occupant experience, particularly in high-density crowd scenarios. The presented CPD method is additionally implemented to run on mobile (Android) devices, allowing on-the-field crowd simulation for event planning.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".