Name
Title | ||
|---|---|---|
Devising and Optimizing Crowd Control Strategies Using Agent-Based Modeling and Simulation |
Abstract | ||
|---|---|---|
Sporting events can attract large crowds who are capable of spurring on their teams. Emotionally charged crowds have a potential to become violent and disruptive, damaging and destroying public properties. Managing and controlling riotous crowds is an important responsibility for police officers to keep public order and safety. Devising and optimizing crowd management strategies is difficult without the knowledge of the scale and situations of the crowd in advance. This paper presents a three-dimensional (3D) simulation framework that simulates a riot and the police response to the riot. The simulation framework is based on agent-based modeling and simulation, consisting of crowd agents, police agents, and transit systems. This study focuses on a specific crowd control strategy: pushing the crowd to the public transit. The police officers in this simulation form police lines which move towards targeted positions pushing the crowd towards the position. In order to optimally disperse the crowd, the police lines move towards public access stations in the transit systems, coercing the crowd to the vicinity of the public transit and containing them there. By directing the crowd into the area where public transit picks up passengers, the crowd would dissipate as crowd occupants got on the transit to leave. The 2011 Vancouver Stanley Cup riot is used in the simulation as a case study. The result of the actual crowd control of the event and that of the crowd control simulation are compared. The framework of this study can be used for other sporting or large crowd events at various locations and for devising different crowd control planning strategies. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EISIC49498.2019.9108875 | 2019 European Intelligence and Security Informatics Conference (EISIC) |
Keywords | DocType | ISBN |
Crowd models,crowd control strategies,agent-based modeling and simulation | Conference | 978-1-7281-6736-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew J. Park | 1 | 15 | 7.07 |
| Lee D. Patterson | 2 | 0 | 0.34 |
| Herbert H. Tsang | 3 | 92 | 19.08 |
| Ryan Ficocelli | 4 | 0 | 0.34 |
| Valerie Spicer | 5 | 23 | 5.46 |
| Justin Song | 6 | 0 | 0.34 |