University of Twente Student Theses


Detection of the crowdedness of a place sensing the devices in the area

Ozaita Araico, Alejandro (2017) Detection of the crowdedness of a place sensing the devices in the area.

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Abstract:Nowadays, more and more people start to live in cities. This change involves the apparition of new problems that could be solved using ICTs, which would lead to ”Smart Cities”. In said cities, all kinds of data is gathered thanks to the sensors all around them, and different applications can be developed, such as the detection of crowded places. The detection of these places can be used, for example, for the prevention of human stampedes, for traffic redirection or for reporting the status of a place remotely. The motivation for this research of the detection of crowded places is caused by the small amount of literature that specifies what was considered a crowded place and that most of the existing methods for their detection distinguished between ”crowded” and ”not crowded” areas arbitrarily. In this thesis, a method for the detection of crowded places calculating the threshold which distinguishes the two mentioned states is presented. For this end, procedures for inferring the number of people, the maximum capacity of an area, and the calculation of the crowdedness threshold using mobility data are described. In conjunction with the description of the methods, their validation in three different areas is also presented. The results of the validation show that the use of a linear regression model for inferring the number of people in a certain area, is an appropriate approach, as the obtained R-Squared value was acceptable, but its performance could be improved by gathering more ground-truth data for the training phase. Regarding the algorithm for the calculation of the maximum capacity, a possible maximum capacity was calculated for each of the analysed areas, but it was inferior to the considered as ground-truth. Finally, thresholds for detecting crowded situations at each of the areas were calculated.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Internet Science and Technology MSc (60032)
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