Scalable distributed simulation of large dense crowds using the real-time framework (RTF)

The simulation of large groups (crowds) of individuals is a complex and challenging task. It requires creating an adequate model that takes into account psychological features of individuals, as well as developing and implementing efficient computation and communication strategies in order to manage the immense computational workload implied by the numerous interactions within a crowd. This paper develops a novel model for a realistic, real-time simulation of large and dense crowds, focusing on evacuation scenarios and the modeling of panic situations. Our approach ensures that both global navigation and local motion are modeled close to reality, and the user can flexibly change both the simulation environment and parameters at runtime. Because of the high computation intensity of the model, we implement the simulation in a distributed manner on multiple server machines, using the RTF (Real-Time Framework) middleware. We implement state replication as an alternative to the traditional state distribution via zoning. We show that RTF enables a high-level development of distributed simulations, and supports an efficient runtime execution on multiple servers.