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Pedestrian flow prediction in extensive road networks using biased observational data

 
: Scheider, S.; May, M.; Rösler, R.; Schulz, D.; Hecker, D.

:

Aref, W.G. ; Association for Computing Machinery -ACM-, Special Interest Group on Spatial Information:
16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008. Proceedings : November 5-7, 2008, Irvine, California, USA
New York: ACM, 2008
ISBN: 978-1-60558-323-5
pp.471-474
International Conference on Advances in Geographic Information Systems (GIS) <16, 2008, Irvine/Calif.>
English
Conference Paper
Fraunhofer IAIS ()

Abstract
In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, in particular with sample selection bias. For this purpose, we introduce s-knn-apriori, an efficient nearest neighbor based spatial mining algorithm that allows prior knowledge and deductive models to be included in a straightforward and easy way.

: http://publica.fraunhofer.de/documents/N-87551.html