Pedestrian flow prediction in extensive road networks using biased observational data

Authors:
Michael May;Simon Scheider;Roberto Rösler;Daniel Schulz;Dirk Hecker
Affiliations:
Fraunhofer IAIS, Sankt, Augustin;University of Münster, Münster;Fraunhofer IAIS, Sankt, Augustin;Fraunhofer IAIS, Sankt, Augustin;Fraunhofer IAIS, Sankt, Augustin
Venue:
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Year:
2008

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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.