1. Crowd Modelling & Simulation
Assoc. Prof. Dr. Hazlina BINTI selamat
NURULAQILLA BINTI KHAMIS
Centre for artificial intelligence
and robotics
(cairo)

2. Presentation Outline Introduction About Crowd Modelling
Mathematical Representation
Agent Motivational Force to Move
Simulation of Agent Motivational Force to Move
Agent Repulsion Force with the Other Agents
Simulation of Agent Repulsion Force with the Other Agents
Agent Repulsion Force with the Obstacle
Simulation of Agent Repulsion Force with the Obstacle
Evading the Other Agents from Ahead
Simulation of Agent Evade the Other Agents
Dijkstra Algorithm
Simulation Model
Possible applications in Robotics Research
Conclusions

3. Where does it normally happen?
Introduction
Crowd definition
Large number of people gathered together
Where does it normally happen?
Entertainment event, sporting event, religious event etc
Consequences
Different types of self-organized behaviours produced

4. About Crowd Modelling Importance by develop crowd model?
What is crowd modelling?
A simulation process to simulate human activities and movement flow
Importance by develop crowd model?
1. Alternative ways to predict crowd flow
2. Experiment with real human (not practical)
Application of crowd model
Assessing professional person (i.e architect, designers, safety engineers) for their design facilities

5. Mathematical Representation
1. Agent motivational force to move
Direction, mass, desired speed and actual speed
2. Agent repulsion force with the other agent
Field of View (FoV), repulsion boundary and safe distance
3. Agent repulsion force with the obstacle
Field of View (FoV) and repulsion boundary
4. Evading the other agents from ahead
Evading to the left or right
Agent motivational force to move
Direction, mass, desired speed and actual speed
2. Agent repulsion force with the other agent
Field of View (FoV) and repulsion boundary
Agent repulsion force with the obstacle
Agent to evade the other agents from ahead
- Evading to the left or right

6. Agent motivational force to move
Based on “Newton Second Law” and summation of forces represent in Eq. (1) – (2)
Eq. (3) represent mathematical model used in representing agent movement
Eq. (1)
Eq. (2)
Eq. (3)

7. Simulation of agent motivational force

8. Agent repulsion force with the other agent
To represent agent repulsion force, magnetic force model applied in this work as represent in Eq. (4)
Including physical characteristics : (a) distance to repulse (b) FoV
Eq. (4)
(a) From top view
(b) From top view

9. Simulation of agent repulse with the other agent

10. Agent repulsion force with the obstacle
For agent repulsion force with the obstacle, by using magnetic force model represented in Eq. (5)
Including physical characteristics : (a) distance to repulse (b) FoV
Eq. (5)
(a) Distance to repulse and FoV

11. Simulation of agent repulse with the obstacle

12. Evading the other agents from ahead
Agent can handle movement in bi-directional flow using agent heading angle to evade to the left or the right
By using concept of angle heading difference & vector rotation as represented in Figure 1 and Eq. (6)
Figure 1 : angle heading difference
--- Eq. (6)

13. Simulation of agent evade the other agents

14. Dijkstra Algorithm
The aim of this feature is to guide an agent in selecting the shortest route towards its target destination
Algorithm adopted in the agent model
Based on node’s exploration in the simulation environment
Simulation environment is segmented into a grid of size “n x n” , pre-defined by the user
Simulation video shows the agent movement by searching optimal path to move towards their target destination in a complex environment

15. Simulation including “Dijkstra Algorithm”

16. Simulation Model
Generates collective types of self-organized behaviours in agent movement under normal situation such as :
Lane formation in a bi-directional movement
Counter flow at the intersection
Movement under complex environment
Integration with path finding feature to select optimal path to move towards target destination
Crowd under emergency situation
Overcrowding at exit door
Produce self-organized behaviours such as arching and clogging
Validation with real data (crowd data)
Simulation model and analysis
Real data
Simulation data
Data analysis (i.e emergency, time taken to evacuate etc)

17. Simulation Model (Cont.)
Simulation model interface using 3D engine & 2D analysis using Matlab

18. Simulation Model (Cont.)
Crowd movement view 1
LANE FORMATION BEHAVIOUR
Crowd movement view 2

19. Simulation Model (Cont.)
Crowd movement view 1 in 2D
COUNTER FLOW AT INTERSECTION
Crowd movement view 2 in 3D

20. Simulation Model (Cont.)
MOVEMENT UNDER COMPLEX ENVIRONMENT

21. Simulation Model (Cont.)
Crowd movement view 1 in 2D
EMERGENCY AT
1 EXIT (DOOR)
Crowd movement view 2 in 3D

22. Simulation Model (Cont.)
REAL DATA (CROWD MOVEMENT FORMING LANES)

23. Possible Application in Robotics Research
Application of mobile robots avoiding obstacle in a similar manner as human.
Moveable obstacles in crowd modelling by using mobile robots.

24. Conclusions
Develop a tool that have a potential to minimize congestion and avoid overcrowding in the environment
This simulation tool is suitable for analysis in building management services for security and safety purposes.