Abstract
Human injuries and casualties at entertaining, religious, or political crowd events often occur due to the lack of proper crowd safety management. For instance, for a large scale moving crowd, a minor accident can create a panic for the people to start stampede. Although many smart video surveillance tools, inspired by the recent advanced artificial intelligence (AI) technology and machine learning (ML) algorithms, enable object detection and identification, it is still challenging to predict the crowd mobility in real-time for preventing potential disasters. In this paper, we propose an intelligent crowd engineering platform using mobility characterization and analytics named ICE-MoCha. ICE-MoCha is to assist safety management for mobile crowd events by predicting and thus helping to prevent potential disasters through real-time radio frequency (RF) data characterization and analysis. The existing video surveillance based approaches lack scalability thus have limitations in its capability for wide open areas of crowd events. Via effectively integrating RF signal analysis, our approach can enhance safety management for mobile crowd. We particularly tackle the problems of identification, speed, and direction detection for the mobile group, among various crowd mobility characteristics. We then apply those group semantics to track the crowd status and predict any potential accidents and disasters. Taking the advantages of power-efficiency, cost-effectiveness, and ubiquitous availability, we specifically use and analyze a Bluetooth low energy (BLE) signal. We have conducted experiments of ICE-MoCha in a real crowd event as well as controlled indoor and outdoor lab environments. The results show the feasibility of ICE-MoCha detecting the mobile crowd characteristics in real-time, indicating it can effectively help the crowd management tasks to avoid potential crowd movement related incidents.
Highlights
Due to the unprecedented scale and speed of urbanization, cities are facing the daunting task of accommodating the urban dynamics
The results show the feasibility of ICE-MoCha detecting the mobile crowd characteristics in real-time, indicating it can effectively help the crowd management tasks to avoid potential crowd movement related incidents
Our work is different where we have focused on tracking crowd mobility indoor and outdoor environments using multiple metrics such as RSSI, beacon count, and time-stamp
Summary
Due to the unprecedented scale and speed of urbanization, cities are facing the daunting task of accommodating the urban dynamics. The concept of smart cities attracts city planners and researchers as it facilitates many smart community services by combining cyber-physical systems and social entities through the wireless, mobile, and intelligent information and communication technologies (ICT). One of the critical service requirements of future cities is the safety management for citizens and communities [1]. The safety management during the densely populated events such as religious, entertainment (such as sport and music), and political gatherings becomes more significant as it happens more frequently and in large scales in modern cities. Unlike static crowd events where a crowd is formed in a specific location, or when a crowd is moving from a location to another (i.e., unidirectional), it requires more space (i.e., less density).
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