An insightful data-driven crowd simulation model based on rough sets

Data-driven crowd simulation with insightful principles is an open, real-world, and challenging task. The issues involved in modeling crowd movement so that agents' decision-making processes can be interpreted provide opportunities to learn about the mechanisms of crowd formation and dispersion and how groups cope with overcoming obstacles. In this article, we propose a novel agent-based simulation algorithm to infer practical knowledge of a problem from the real world by modeling the domain knowledge available to an agent using rough sets. As far as we know, the method proposed in our work is the first approach that integrates a well-established agent-based simulation model of social forces, an insightful knowledge representation using rough sets, and Bayes probability inference that models the stochastic nature of motion. Our approach has been tested on real datasets representing crowds traversing bottlenecks of varying widths. We also conducted a test on numerous artificial datasets involving 1,000 agents. We obtained satisfactory results that confirm the effectiveness of the proposed method. The dataset and source codes are available for download so our experiments can be reproduced.