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Incorporating Mobility Patterns in Pedestrian Quantity Estimation and Sensor Placement

 
: Liebig, Thomas

:
Preprint urn:nbn:de:0011-n-2212353 (4.0 MByte PDF)
MD5 Fingerprint: aa8012ff1bf7e019041fa388937c17b3
Erstellt am: 9.1.2013


Nin, J.:
Citizen in Sensor Networks. 1st International Workshop, CitiSens 2012 : Montpellier, France, August 27, 2012. Revised Selected Papers
Berlin: Springer, 2013 (Lecture Notes in Computer Science 7685)
ISBN: 978-3-642-36073-2
ISBN: 978-3-642-36074-9
S.67-80
International Workshop on Citizen Sensor Networks (CitiSens) <1, 2012, Montpellier>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
pedestrian quantity estimation; traffic pattern; Gaussian process regression; bluetooth tracking

Abstract
Pedestrian quantity estimation receives increasing attention and has important applications, e.g. in location evaluation and risk analysis. In this work, we focus on pedestrian quantity estimation for event monitoring. We address the problem (1) how to estimate quantities for unmeasured locations, and (2) where to place a bounded number of sensors during different phases of a soccer match. Pedestrian movement is no random walk and therefore characteristic traffic patterns occur in the data. This work utilizes traffic pattern information and incorporates it in a Gaussian process regression based approach. The empirical analysis on real world data collected with Bluetooth tracking technology during a soccer event at Stade des Costières in Nîmes (France) demonstrates the benefits of our appro ach.

: http://publica.fraunhofer.de/dokumente/N-221235.html