Risk-based crowd massing early warning approach for public places: A case study in China

Beijing municipal government plans to establish a comprehensive real-time monitoring and risk identification technological system basically covering the targeted districts with crowded pedestrian traffic. The system aims to send out alarm in crowded places through analyzing quantitative data from intelligent real-time monitoring devices installed in key locations. An early warning signal is derived from the judgment of current status of the crowd, and this research aims to explain the mechanism of how to judge crowd status. Classically risk is defined as multiplication of impact and probability of occurrence of an event. Here, a 2-D risk matrix is established to judge the crowd status by probability (duration of the pedestrian status) plus consequence (strength of the pedestrian status). After installed intelligent monitoring devices in key locations, the online observations of crowd movement within the targeted district can produce the early warning alarm ahead of time. Such alarm is expected to provide the local administrator 10 min to prevent potential undesired chaos by taking proper controlling actions. This paper is a case study in a specific area in China. This system can be used in many areas such as sporting events and religious gatherings.