Simulation of collision avoidance behavior in crowd movement by data-driven approach
This work is significant for improving the safety and realism of crowd movement simulations, which is crucial for pedestrian safety management and facility layout optimization.
This paper addresses the high collision rates in data-driven crowd movement simulations, particularly in bidirectional flows. By incorporating a novel lateral-acceleration-based collision mechanism into the loss function of a Generative Adversarial Network (CPGAN), the model significantly reduced opposite-direction pedestrian collision rates to levels comparable with controlled experiments.
Crowd movement simulation is essential for pedestrian safety management and facility layout optimization. Data-driven models enhance trajectory prediction accuracy under Euclidean metrics, yet they suffer from excessively high collision rates, especially in bidirectional and multidirectional flows. In this paper, we establish a novel data-driven crowd simulation model that incorporates the pedestrian collision mechanism into the loss function to reduce collisions. A new lateral-acceleration-based collision loss function and a Voronoi-based motion feature extraction approach are proposed. The model is based on a Generative Adversarial Network (GAN) architecture and is termed CPGAN (Collision-Penalized GAN). We evaluate CPGAN in bidirectional flow scenarios, which involve frequent collision avoidance behaviors. Results show that the proposed lateral-acceleration-based collision loss significantly reduces opposite-direction pedestrian collision rates to levels comparable with controlled experiments. CPGAN effectively simulates bidirectional flow, reproducing lane formation and N-t curves. The research outcomes can provide inspiration for integrating pedestrian dynamics mechanisms into loss functions in data-driven crowd simulation.