Simulation of collision avoidance behavior in crowd movement by data-driven approach

Researchers propose CPGAN, a collision-penalized generative adversarial network that improves crowd simulation accuracy by incorporating pedestrian collision mechanisms directly into the model's loss function. The approach significantly reduces collision rates in bidirectional pedestrian flows while accurately reproducing real-world phenomena like lane formation.

This research addresses a critical limitation in data-driven crowd simulation models that traditionally optimize for trajectory prediction accuracy without adequately accounting for collision avoidance—a fundamental aspect of real pedestrian behavior. The CPGAN framework introduces a lateral-acceleration-based collision loss function that penalizes trajectories intersecting in space and time, bridging the gap between mathematical accuracy and physical realism.

The work builds on established pedestrian dynamics research while advancing machine learning applications in simulation. Previous data-driven approaches achieved low Euclidean errors in trajectory prediction yet produced unrealistic collision rates exceeding 20% in high-density scenarios. By embedding collision penalties during training, CPGAN forces the model to learn emergent behaviors like lane formation and lane switching without explicit programming, demonstrating that GAN architectures can capture complex crowd dynamics when properly constrained.

For practitioners in urban planning, facility design, and emergency management, this advancement enables more reliable simulations for safety assessments and infrastructure optimization. The model's ability to reproduce bidirectional flow phenomena validates its utility in real-world applications where pedestrian safety directly impacts public health outcomes. The Voronoi-based feature extraction method also provides a generalizable approach for incorporating spatial constraints into neural network models.

Future development should test CPGAN's performance across varied scenarios—high-density crowds, complex geometries, and mixed-flow situations—while exploring computational efficiency for real-time applications. Integration with actual facility data could further validate whether the model generalizes beyond controlled experimental conditions.

• →CPGAN reduces bidirectional pedestrian collision rates to levels matching controlled laboratory experiments through collision-aware loss functions
• →The lateral-acceleration-based collision penalty enables GANs to learn realistic crowd dynamics without explicit behavioral programming
• →Voronoi-based motion feature extraction provides a generalizable method for embedding spatial constraints in data-driven models
• →The approach reproduces emergent phenomena like lane formation and N-t curves, validating physical realism beyond trajectory accuracy
• →Results have direct applications for urban planning, facility design, and safety management in high-density pedestrian environments