Indoor crowd modeling refers to the computational and mathematical representation of human collective behavior, movement, and density dynamics within enclosed spaces, encompassing both pedestrian flow simulation and occupancy estimation. It matters for WiFi/CSI sensing research because understanding and predicting crowd patterns enables the validation and benchmarking of passive sensing systems against ground-truth behavioral models, and supports applications such as emergency response, space optimization, and safety monitoring. Key variants include physics-inspired agent-based approaches such as social force models that simulate individual pedestrian interactions and self-organization under panic or normal conditions, as well as data-driven queueing network models that capture stochastic arrival, service, and congestion dynamics within structured indoor environments like hospitals or transit hubs.
Source Papers
- Continuum theory for pedestrian traffic flow: Local route choice modelling and its implications ↗ — Continuum theory for pedestrian traffic flow: Local route ch
- Dimensionless numbers reveal distinct regimes in the structure and dynamics of pedestrian crowds ↗ — Dimensionless numbers reveal distinct regimes in the structu
- The Flow of Human Crowds ↗ — The Flow of Human Crowds