Published Paper Details:

Crowd control is a critical aspect of public safety management, particularly in large gatherings such as festivals, sports events, protests, and transportation hubs. Traditional crowd management methods often rely on manual monitoring and human judgment, which can be prone to error and inefficiency. This research explores how Artificial Intelligence (AI) can be utilized to optimize crowd control by analyzing real-time data, predicting crowd behavior, and automating decision-making processes. The study focuses on integrating AI techniques such as computer vision, machine learning, and predictive analytics to monitor crowd density, detect anomalies, and prevent potential hazards. By leveraging these technologies, authorities can achieve faster response times, improved situational awareness, and enhanced safety outcomes. The proposed framework demonstrates that AI-powered systems can significantly reduce the risks associated with overcrowding and contribute to more efficient crowd management strategies [1]