Early warning and managing system for regulating high-traffic periods in subway stations

Addressing the persistent issue of passenger overflow during peak hours in subway stations, this paper introduces an innovative adaptive early warning system. The system utilizes real-time data analytics and advanced predictive algorithms to monitor passenger volumes dynamically and generate timely alerts when traffic becomes excessive. These alerts are specifically designed to provide station staff with vital information that enables them to respond promptly. As a result, the normal operation of the subway station can be ensured, along with the safety of the passengers. The effectiveness and accuracy of this proposed system are demonstrated through a series of case studies. These studies confirm the system’s capability to enhance the management of passenger flow significantly. This research has the potential to revolutionize contemporary practices in subway station management and contribute to safer, more efficient public transportation systems.