Agent-based simulation is a computational modeling approach in which individual entities (agents) are each governed by defined behavioral rules and local interaction dynamics, allowing complex collective phenomena such as crowd movement to emerge from the bottom up. In the context of crowd safety and flow forecasting, it matters because it enables high-fidelity, microscopic representation of thousands of pedestrians simultaneously, supporting both scenario planning and real-time predictive tasks when coupled with data assimilation techniques such as particle filters. Key variants include force-based models, which simulate physical and social forces acting on each agent, and can be integrated with sequential latent parameter estimation to continuously update simulation states from aggregate observational data.
Source Papers
- A roadmap for the future of crowd safety research and practice: Introducing the Swiss Cheese Model of Crowd Safety and the imperative of a Vision Zero target ↗ — A roadmap for the future of crowd safety research and practi
- Controlling inter-particle distances in crowds of motile, cognitive, active particles ↗ — Controlling inter-particle distances in crowds of motile, co
- Crowd flow forecasting via agent-based simulations with sequential latent parameter estimation from aggregate observation ↗ — Crowd flow forecasting via agent-based simulations with sequ
- Modelling physical contacts to evaluate the individual risk in a dense crowd ↗ — Modelling physical contacts to evaluate the individual risk
- Social force model for pedestrian dynamics ↗ — Social force model for pedestrian dynamics
- State-of-the-art crowd motion simulation models ↗ — State-of-the-art crowd motion simulation models
- The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics ↗ — The Walking Behaviour of Pedestrian Social Groups and Its Im