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 presented in @cite_16 @cite_9 presents another approach to analyze people attendance to special events on the basis of CDRs coming from the AirSage ( www.airsage .com ) platform. In this work, they segment users' traces to identify those places where a user stops. If this place, coincides with the place of the event and if the duration of the stop is at least the 70 the event, the user is classified as attending the event. On this basis they are able to analyze the attendance to specific events. However, they claim: Estimating the actual number of attendees is still an open problem, considering also that ground truth data to validate models is sometime absent or very noisy '' and do not perform quantitative analysis in this direction.
@cite_9 @cite_16
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This paper deals with the analysis of crowd mobility during special events. We analyze nearly 1 million cell-phone traces and associate their destinations with social events. We show that the origins of people attending an event are strongly correlated to the type of event, with implications in city management, since the knowledge of additive flows can be a critical information on which to take decisions about events management and congestion mitigation. The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not natively provide any additional contextual information like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks with the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activities performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability measure based on the gravity law, we infer the activity performed. We experimented and evaluated the method in a real case study of car trajectories, manually annotated by users with their activities. Experimental results are encouraging and will drive our future works. This paper presents a strategy to evaluate long-distance travel patterns by tracking cellular phone positions. The authors first note that long-distance trips are generally under-reported in typical household surveys, because of relative low frequency of these trips. Yet transportation analysis and travel demand forecasting require data, including that for long-distance trips, in order to model the decisions that people make related to travel. They stress that their suggested approach allows passive data collection on many travelers over a long period of time at low costs. They present results of a study in Israel, conducted in 2007, that included an average sample of 10,200 cell phone numbers per week for 16 weeks. The tracking system was based on recording events that contain a change in the position of the cell phone with respect to a given antenna. The method was specifically designed to capture long distance trips, as part of the development of a national demand model conducted for the Economics and Planning Department of the Israel Ministry of Transport. Using this method, origin–destination tables can be constructed directly from the cellular phone positions. The authors conclude that this model offers the advantage of monitoring travel demand at the aggregate level and thus could be useful in several transportation and land use applications. In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in videos. The proposed approach has three main steps. First, in order to learn the event structure from training videos, we automatically encode the sub-event dependency graph, which is the learnt event model that depicts the conditional dependency between sub-events. Second, we pose the problem of event detection in novel videos as clustering the maximally correlated sub-events using normalized cuts. The principal assumption made in this work is that the events are composed of a highly correlated chain of sub-events that have high weights (association) within the cluster and relatively low weights (disassociation) between the clusters. The event detection does not require prior knowledge of the number of agents involved in an event and does not make any assumptions about the length of an event. Third, we recognize the fact that any abstract event model should extend to representations related to human understanding of events. Therefore, we propose an extension of CASE representation of natural languages that allows a plausible means of interface between users and the computer. We show results of learning, detection, and representation of events for videos in the meeting, surveillance, and railroad monitoring domains.