Finding moving flock patterns among pedestrians through collective coherence |
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| Authors: | Monica Wachowicz Rebecca Ong Chiara Renso Mirco Nanni |
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| Institution: | 1. Department of Geodesy and Geomatics Engineering , University of New Brunswick , Fredericton, NB, Canada monicaw@unb.ca;3. Knowledge Discovery and Data Mining Laboratory, Department of Computer Science , University of Pisa , Pisa, Italy;4. Knowledge Discovery and Data Mining Laboratory, ISTI-CNR , Pisa, Italy |
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| Abstract: | Tracking technologies are able to provide high-resolution movement data that can advance research in different fields, such as tourism management. In this specific field, developing methods to extract moving flock patterns from such data are particularly relevant to enable us to improve our knowledge of the nature of recreational use interactions, which is crucial for a good management of attractions and for designing sustainable development policies. However, ‘flocking’ has been usually associated with the form of collective movement of a large group of birds, fish, insects and certain mammals as well. Very few research efforts have been devoted in finding flock patterns associated with pedestrian movement. In this work, we propose a moving flock pattern definition and a corresponding extraction algorithm based on the notion of collective coherence. We use the term collective coherence to refer to the spatial closeness over some time duration with a minimum number of members. Furthermore, we evaluate the proposed algorithm by applying it to two different pedestrian movement datasets, which have been gathered from visitors of two recreational parks. The results show that the algorithm is capable of extracting moving flock patterns, disqualifying the patterns with flock members that remain stationary in a common place during the considered time interval. |
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| Keywords: | flock patterns pedestrian dynamics flock algorithm flock discovery moving flock |
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