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

• Main Authors: Kolivand, Hoshang, Rahim, Mohd. Shafry, Sunar, Mohd. Shahrizal, Azizul Fata, Ahmad Zakwan, Wren, Chris
• Format: Article
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2020
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/90240/1/MohdShafryMohdRahim2020_AnIntegrationofEnhancedSocialForce.pdf; http://eprints.utm.my/id/eprint/90240/; http://dx.doi.org/10.1007/s00521-020-05385-6
• Institution: Universiti Teknologi Malaysia

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.