Diversified Crowd Evacuation Method in Large Public Places

Abstract

Realistic crowd simulation is an important issue for crowd management, public space design, and architectural and urban planning. In this paper, we present a novel approach for planning and directing heterogeneous large-scale crowd in public places. We firstly extract group features from real-world video, and transfer them to the virtual crowd members. On this basis, we perform the crowd evacuation by combining the global path planning and collide avoidance strategy. Our method efficiently produces the most promising paths in both time and space and offers a reasonable distribution of the crowd' members over these paths such that their average evacuation time is minimized based on the probabilistic roadmaps approach. The generated paths are combined with reciprocal velocity obstacle method to handle collisions and generate the final motions of the individuals. We demonstrate its potential on large public places with 10000 individuals and evaluate the results from our simulations using some quantitative quality metrics. In practice, our method can interactively simulate large crowds with thousands of individuals on a desktop PC and naturally generates a diverse set of emergent behaviors.

Keywords

• Crowd simulation
• collide avoidance
• reciprocal velocity obstacle
• probabilistic roadmap