Using Graphs of Queues and Genetic Algorithms to Fast Approximate Crowd Simulations

The use of Crowd Simulation for re-enacting different real life scenarios has been studied in the literature. In this field of research, the interplay between ambient assisted living solutions and the behavior of pedestrians in large installations is highly relevant. However, when designing these simulations, the necessary simplifications may result in different ranges of accuracy. The more realistic the simulation task is, the more complex and computational expensive it becomes. We present an approach towards a reasonable trade-off: given a complex and computational expensive crowd simulation, how to produce fast crowd simulations whose results approximate the results of the detailed and more realistic model. These faster simulations can be used to forecast the outcome of several scenarios, enabling the use of simulations in decision-making methods. This work contributes with a simplified faster simulation model that uses a graph of queues for modeling an environment where a set of agents will navigate. This model is configured using Genetic Algorithms (GA) applied to data obtained from complex 3D crowd simulations. This is illustrated with a proof-of-concept scenario where a 3D simulation of one floor of a faculty building, with its corresponding students, is re-enacted in the network of queues version. The success criteria are achieving a similar total number of people in particular floor areas along the simulation in both the simplified simulation and the original one. The experiments confirm that this approach approximates the number of people in each area with a sufficient degree of fidelity with respect to the results that are obtained by a more complex 3D simulator