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, Sr., 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