Centroidal Particle Dynamics: An Explicit Model of Pedestrian Personal Space for the Simulation of Short-Range Collision-Avoidance and Emergent Motion Patterns in Dense Crowds

Computer simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. State-of-the-art particle-based crowd methods assume and aim for collision-free trajectories. That is an idealistic yet not overly realistic expectation, as near-collisions increase in dense and rushed settings compared to typically sparse pedestrian scenarios. We propose Centroidal Particle Dynamics (CPD), a method that explicitly models the compressible personal space area surrounding each entity (~0.8m-1.0m radius) to inform its local pathing and collision avoidance decisions. While personal space has traditionally been modeled as a fixed radius, the reality is that it often changes in response to the surrounding context. For instance, in cases of congestion, entities tend to share more of their personal space than they normally would, simply out of necessity (e.g. passing through a crowded gate or boarding a train). Likewise, entities travelling at higher speeds (e.g. strolling, running) tend to expect a larger area ahead of them to be their personal -unoccupied- space. We illustrate how our proposed agent-based method for local dynamics can reproduce several key emergent dense crowd phenomena at the microscopic level (e.g. emergent lane formation in bidirectional flow and arching near congested gateways) with higher congruence to real trajectory data and with more visually convincing collision avoidance paths than the existing state-of-the-art.We further show how CPD can be efficiently computed on consumer-grade graphics hardware (GPU), achieving interactive frame rates when simulating thousands of crowd entities in the scene, thus making it suitable and ready-for-use in our target applications of entertainment and interactive media projects (i.e. film, gaming, and educational media). Lastly, we discuss crowd motion validation, and how to increase confidence in CPD, potentially making it also suitable for use in safety-critical applications, including urban design, evacuation analysis, and crowd safety planning.