Crowd behaviour in Canadian football stadia — Part 1: Data collection
Bibliographic record
Abstract
Large crowd sizes at stadia events require an in-depth consideration of human behaviour, since the reliability of egress models in design depends on the confidence of the input data. However, there is little contemporary public data surrounding crowd behaviour, and its implementation into pedestrian movement models, particularly focused on Canadian demographics in stadia. A novel data collection (Part 1) and subsequent egress validation modelling of a Canadian stadium were completed to examine the variability of simulations with behavioural inputs (Part 2). The demographic distribution, pedestrian speed, exit and route choice, and areas of congestion were quantified using high resolution cameras. Behaviourally, pedestrians exited the stadium where they entered which created high levels of cross flow. It was observed that contemporary walking speed profiles for stadia will differentiate from classical profiles especially with reflection of demographic distribution by as much as 31%. Individual walking speeds, while highly variable, impacted overall egress time. Crowd density also being a factor that further reduced their speed.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".