We present a methodology to estimate the number of attendees to events happening in the city from cellular network data. In this work we used anonymized Call Detail Records (CDRs) comprising data on where and when users access the cellular network. Our approach is based on two key ideas: (1) we identify the network cells associated to the event location. (2) We verify the attendance of each user, as a measure of whether (s)he generates CDRs during the event, but not during other times. We evaluate our approach to estimate the number of attendees to a number of events ranging from football matches in stadiums to concerts and festivals in open squares. Comparing our results with the best groundtruth data available, our estimates provide a median error of less than 15 of the actual number of attendees.
The work in @cite_13 is very interesting and closer to our approach. They use a Bayesian model to localize the source of CDRs. Then, they compute the probability @math of each user to participate an event as @math . Where @math is the fraction of time in which the user is in the event area at the event time. @math is the fraction of time in which the use is in the event area at other times. Finally, they use an outlier detection mechanism (based on a z-score) to classify users as participants to an event. Unfortunately, they use the approach only to identify an event and not to estimate the actual attendance.
@cite_13
2007022751
The unprecedented amount of data from mobile phones creates new possibilities to analyze various aspects of human behavior. Over the last few years, much effort has been devoted to studying the mobility patterns of humans. In this paper we will focus on unusually large gatherings of people, i.e. unusual social events. We introduce the methodology of detecting such social events in massive mobile phone data, based on a Bayesian location inference framework. More specifically, we also develop a framework for deciding who is attending an event. We demonstrate the method on a few examples. Finally, we discuss some possible future approaches for event detection, and some possible analyses of the detected social events.