Modelling Crowd Dynamics and Crowd Management Strategies
Bibliographic record
Abstract
A crowd is a large number of people who gather at a specific location at the same time with or without shared agendas and/or emotions. A crowd is formed at sporting, holiday, religious, or political events. Proper management of a crowd is one of the major duties for the civic agencies such as police and fire departments to maintain public order and safety. When such management is ill-prepared and poorly executed, it can result in chaotic situations with human injuries and fatalities as well as damage to public properties. Good crowd management requires a good modelling of crowd dynamics and devising strategies accordingly. This paper presents the modelling and simulations of crowd dynamics that resembles the actual crowd behaviours using the social force model. With the realistic crowd dynamics, different scenarios and crowd management strategies were tested for optimal crowd flows. Our framework can be used to test various scenarios of an event that attracts a large crowd and devise suitable strategies for the crowd management.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".