An integration of enhanced social force and crowd control models for high-density crowd simulation

Kolivand, H, Rahim, MS, Sunar, MS, Fata, AZA and Wren, C (2020) An integration of enhanced social force and crowd control models for high-density crowd simulation. Neural Computing and Applications. ISSN 0941-0643

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Abstract

Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population.

Item Type:	Article
Uncontrolled Keywords:	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science and Mathematics
Publisher:	Springer
Date of acceptance:	24 September 2020
Date of first compliant Open Access:	26 October 2020
Date Deposited:	26 Oct 2020 12:13
Last Modified:	04 Sep 2021 06:33
DOI or ID number:	10.1007/s00521-020-05385-6
URI:	https://researchonline.ljmu.ac.uk/id/eprint/13815

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