On cleaning strategies for WiFi positioning to monitor dynamic crowds

Abstract

Monitoring open crowded areas is fundamental for policy makers to set up the proper measures for people security and safety. Different techniques have been developed to tackle this issue. The most relevant approaches, which are currently available to estimate the number of attenders, are based on the size of the area hosting the event, the count of people passing through specific points, and the employment of satellite images. All these techniques roughly estimate static crowds, but they may be limited by different factors such as the availability of satellite images, the cost of dedicated unmanned vehicles, and the capability to set up multiple counting points. In order to fill this gap, a tool to monitor dynamic crowds, based on WiFi positioning, is presented. The tool allows not only to assess the total number of people attending an event, but also to monitor their spatiotemporal distribution. In particular, the impact of the cleaning strategies on both the estimated number of participants and their spatiotemporal distribution is analyzed. The proposed approach is demonstrated using real data collected during the JRC Open Day 2016. From the results, the need of a clear strategy to identify real users in order to avoid misleading results emerges. Moreover, a proper setting of the thresholds used for the identification criteria is required. Such thresholds need to be set according to the dimension of the site, the geography of the WiFi network, and the duration of the event.

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