Crowd movement monitoring in academic buildings: a reinforcement learning approach

In academic institutions, the gathering of students within buildings significantly influences the ecological parameters of these spaces. Understanding and enhancing the environmental impact of academic buildings requires monitoring ecological parameters and crowd flow. The job of monitoring large-scale crowds and environmental conditions is a difficult one to do without difficulty. This study introduces reinforcement learning-based lightweight crowd flow measurement (RL-CFM) for real-time crowd flow tracking in institutional buildings. RL-CFM enables prompt responses to changes in crowd dynamics, enhancing its effectiveness during emergencies. The proposed RL-CFM periodically searches for smartphone-enabled requests, providing insights into crowd movement. Implemented and tested in an institutional building under real-world conditions, RL-CFM was installed in various locations, including the evacuation passage on the ground floor and two classrooms on the first floor. The study explores ecological parameters like temperature and CO2 concentration in the evacuation passage, considering various modes of smartphone operation that reflect people’s walking behavior. The RL-CFM’s performance is evaluated using different smartphone models with varying walking speeds, revealing a tracking accuracy of 94.32% in the Wi-Fi registered mode.