Abstract
Some crowd management systems analyze crowd dynamics, such as density and flow, in isolation from the physical environment. The disclosed system can couple a Dynamic Crowd State Vector, which may quantify real-time crowd metrics, with a Hyper-Local Condition Vector, which may quantify environmental factors such as surface integrity, passage width, and lighting. A machine learning model can process the real-time interaction between these two vectors to generate a unified, predictive risk score for a specific geographic zone. The resulting coupled analysis may facilitate the proactive identification of context-specific hazards, such as moderate crowds in some conditions, which may aid in more precise risk assessment and timely interventions to manage crowd-related incidents.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kumar S R, Mithun, "A System for Predictive Crowd Management Using Coupled Analysis of Crowd State and Environmental Information", Technical Disclosure Commons, (July 14, 2025)
https://www.tdcommons.org/dpubs_series/8356