Self-Organized Behaviour in a Modified Multi-Agent Simulation Model Based on Physical Force Approach
Keywords:
Crowd Modelling, Multi-Agent Model, Physical Force Approach, Self-Organized BehaviourAbstract
The multi-agent simulation models are very useful in predicting crowd behaviours, where experiments with human are too dangerous. However, works that include intelligence in the physical force-based model are very limited. Moreover, most works based on the physical force-based model are only restricted to producing behaviours of crowds in emergency situations. Utilizing a simpler mathematical representation, this paper proposes a modification to an existing crowd simulation model based on the physical force approach. The proposed method incorporates the concept of magnetic force interaction into the existing social force model of an agent movement. It simulates the agent’s interaction within their boundaries, preventing any collisions from occurring and to follow others when the agents move in a same direction. The proposed method incorporated agents’ intelligence to choose the shortest path in their movements towards their destinations. When the number of agents increases in the simulation environment, the model is able to produce a self-organized behaviour, such as the lane formation behaviour pattern when the agents are in a bi-directional movement as well as in a counter flow movement at intersections.References
V. Mahalleh, H. Selamat, F. Sandhu, and N. Khamis, “Improved Crowd Psychological Model and Control,” Jurnal Teknologi, vol. 78, no. no 6-13, pp. 120–128, 2016.
J. Lind, “MASSIVE: Software Engineering for Multiagent Systems,” 1999.
C. Burstedde, K. Klauck, A. Schadschneider, and J. Zittartz, “Simulation of pedestrian dynamics using a two-dimensional cellular automaton,” Phys. A Statistical Mechanics and its Applications, vol. 295, no. 3–4, pp. 507–525, 2001.
Cabinet Office, Understanding Crowd Behaviours, vol. 59. 2009.
N. Khamis, H. Selamat, and R. Yusof, “Simulation of Agent Movement with a Path Finding Feature Based on Modification of Physical Force Approach,” Applied Computational Science, 2014.
S. Zhou, D. Chen, W. Cai, L. Luo, M. Y. H. Low, F. Tian, V. S.-H. Tay, D. W. S. Ong, and B. D. Hamilton, “Crowd modeling and simulation technologies,” ACM Transactions on Modeling and Computer Simulation, vol. 20, no. 4, pp. 1–35, 2010.
S. Okazaki and S. Matsushita, “A study of simulation model for pedestrian movement,” First International Conference on Engineering for Crowd Safety, no. 1, pp. 271–280, 1993.
D. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic.,” Nature, vol. 407, no. 6803, pp. 487–490, 2000.
Z. Daoliang, Y. Lizhong, and L. Jian, “Exit dynamics of occupant evacuation in an emergency,” Physica A Statistical Mechanics and its Applications, 2006, vol. 2, pp. 501-511
L. Huang, T. Chen, and H. Yuan, “Simulation study of evacuation in high-rise buildings,” in Transportation Research Procedia, 2014, vol. 2, pp. 518–523.
N. Abdullasim, A. Basori, and M. Salam, “Velocity Perception: Collision Handling Technique for Agent Avoidance Behavior,” Indonesian Journal of Electrical Engineering and Computer Science, 2013, pp. 2264-2270.
H. Ismail, F. S., Yusof, R., Khalid, M., Ibrahim, Z., & Selamat, “Performance evaluation of self organizing genetic algorithm for multi-objective optimization problem,” ICIC Express Letters, vol. 6, no. 1, pp. 1–7, 2012.
J. J. Fruin, “Pedestrian Planning and Design,” Elevator World Inc, vol. 77, no. 4, pp. 556–561, 1971.
N. Khamis, H. Selamat, and R. Yusof, “Modification of Physical Force Approach for Simulating Agent Movement with Collective Behavior,” Jurnal Teknologi, vol. 2, pp. 7–11, 2015.
M. Sniedovich, “Dijkstra’s algorithm revisited: the dynamic programming connexion,” Control Cybernetics, 2006, vol. 3, pp. 599.
L. Xiao-Yan and C. Yan-Li, “Application of Dijkstra Algorithm in Logistics Distribution Lines,” Third International Symposium on
Computer Science and Computational Technology, 2010, pp. 48-50.
M. Bosse, P. Newman, J. Leonard, M. Soika, W. Feiten, and S. Teller, “An Atlas framework for scalable mapping,” IEEE International Conference Robotics Automation, 2003, vol. 2, pp. 1899–1906.
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