Current status and future directions for crowd management using machine learning

Abstract: Crowd management is a critical aspect of public safety and efficient space utilization in areas with high pedestrian traffic such as urban centers, sports stadiums, transportation hubs, and during large-scale events. Traditional crowd management techniques often rely on manual surveillance and estimation, leading to challenges in real-time decision-making and risk mitigation. The advent of machine learning (ML) has introduced new opportunities to enhance crowd management strategies. This paper explores the application of ML techniques in crowd management, focusing on how these technologies can be utilized for crowd density estimation, flow prediction, behavior analysis, and risk assessment. Our findings demonstrate that ML-based crowd management systems can significantly improve the accuracy and efficiency of crowd monitoring and control. These systems offer real-time analytics, enhanced situational awareness, and predictive capabilities, leading to more informed decision-making by authorities. We also address the challenges faced in implementing these systems, including data privacy concerns, the need for large and diverse datasets, and the importance of system robustness and reliability. Finally, the paper outlines future research directions in this field. These include the integration of more advanced ML techniques like deep learning, the exploration of real-time adaptive learning systems for dynamic crowd management, and the ethical considerations and regulatory compliance in the deployment of such technologies. By harnessing the power of machine learning, crowd management can be transformed into a more proactive, data-driven, and responsive practice, contributing significantly to public safety and efficient space management.