Effective Crowd Management in a T-Intersection
Bibliographic record
Abstract
A crowd assembles together for a common goal or purpose at holiday, sporting, religious, or political events. Some events might be peaceful and celebratory, while others might be hostile or even riotous. Different crowd management strategies need to be devised and executed depending on the event’s characteristics, crowd, and environmental configurations. Failure of such strategies may result in human casualties and property damages. Considering all possible cases, law enforcement agencies must plan how to manage a large crowd in advance. This paper presents crowd management strategies in a T-intersection using barricades for peaceful, celebratory events such as the Honda Celebration of Light in Vancouver, Canada. Agent modelling and simulation technique was utilized to simulate the crowds’ exits after the event. Crowd simulations with/without various arrangements of barricades were conducted, and their results were discussed. The experimental results show that forcing the crowds to move to a specific route in a T-intersection leads to the fastest dispersion.
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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".