A Novel Approach to Crowd Management Using Machine Learning

Crowd Management Systems (CMS) leverage artificial intelligence (AI) to enhance public safety by organizing and monitoring crowd dynamics in various settings. This study examines AI-based tools—machine learning, computer vision, and real-time data processing—used to predict crowd behavior and manage risks effectively. CMS utilizes predictive models and video analytics to autonomously monitor crowd density, detect potential hazards, and enable prompt emergency responses across domains such as stadiums, airports, and smart city infrastructure. By analyzing extensive streams of video and sensor data, CMS improves response accuracy and optimizes resource allocation. As a component of smart city initiatives, CMS integrates data from multiple sources, such as transit and emergency services, continuously refining its efficiency. AI-powered CMS systems are critical for safe, efficient crowd management, with applications in event automation, congestion control, and rapid evacuation planning in high-traffic public spaces.