Crowd Control Strategy Framework Using Real-Time 3D Simulations
Bibliographic record
Abstract
A crowd can be formed for a political purpose or at a sporting event. They can be emotionally charged and become violent, which may result in creating public disorder and damaging/destroying properties in a public space. Managing and controlling this kind of disruptive crowd is an important responsibility for police officers to keep the public order and safety. However, devising a practical crowd control strategy in advance is difficult, not knowing the scale of the crowd and their situation. We have developed a crowd control strategy framework system called Urban Behaviour Simulator (UrBeSim) for police who devise crowd control strategies. It is a comprehensive system that automatically generates a three dimensional virtual environment based on the given latitude and longitude. It also generates different kinds of crowd based on a well-studied crowd model. Some crowd models simulate an angry crowd (instigators and radicals) who provokes a riot. Other models are conservatives and guardians who try to keep away from a riot. The system provides a mechanism to place riot police (on foot and on horse) and police cars to try different strategies to control the crowd. The UrBeSim has been evaluated with the case study of the Stanley Cup Riot and an expert's review. Benefits, shortcomings, and future plan of the system are discussed.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".