Artificial Neural Networks in Smart Stadiums: Optimizing Crowd Flow, Safety, and Fan Engagement

Abstract

Modern sports entertainment increasingly relies on cutting-edge technologies to enhance audience engagement and ensure stadium safety. This work discusses the integration of smart stadium systems powered by Artificial Neural Networks (ANNs) to improve spectator experience, security, and crowd management. IoT sensors deployed across stadium zones capture real-time data on crowd density, environmental conditions, and spectator behavior. This information is processed through ANN-based predictive modeling and decision-making algorithms to generate actionable insights. A key focus of this approach is dynamic fan engagement. By analyzing preferences, historical interactions, and real-time context, ANNs enable personalized services for fans through their devices, including customized notifications, offers, and immersive augmented reality experiences. At the same time, ANNs enhance safety by continuously monitoring IoT sensor data streams to detect anomalies and risks. For example, they can identify overcrowded areas or unusual temperature fluctuations and automatically alert security and emergency systems to safeguard visitors and staff. Furthermore, ANNs provide valuable support for crowd management and resource optimization. By leveraging attendance statistics, crowd dynamics, weather forecasts, and transportation updates, stadiums can make informed, real-time decisions. This enables adaptive control of seating arrangements, concession stand operations, and entry/exit flows, ultimately improving both efficiency and the overall fan experience.