Density-based evolutionary framework for crowd model calibration

• We propose an evolutionary framework to calibrate crowd models, so that the simulated crowd behaviors can closely match the objective behaviors.
• We introduce a density-based matching scheme to automatically evaluate the simulated crowd behaviors at a macroscopic level.
• We propose a hybrid search mechanism based on differential evolution to reduce the computational time.
• We design different simulation scenarios to test our framework, and the results demonstrate that our algorithm is effective and efficient for crowd model calibration.

Crowd modeling and simulation is an important and active research field, with a wide range of applications such as computer games, military training and evacuation modeling. One important issue in crowd modeling is model calibration through parameter tuning, so as to produce desired crowd behaviors. Common methods such as trial-and-error are time consuming and tedious. This paper proposes an evolutionary framework to automate the crowd model calibration process. In the proposed framework, a density-based matching scheme is introduced. By using the dynamic density of the crowd over time, and a weight landscape to emphasize important spatial regions, the proposed matching scheme provides a generally applicable way to evaluate the simulated crowd behaviors. Besides, a hybrid search mechanism based on differential evolution is proposed to efficiently tune parameters of crowd models. Simulation results demonstrate that the proposed framework is effective and efficient to calibrate the crowd models in order to produce desired macroscopic crowd behaviors.