In this paper, the authors 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, the authors 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.
Pedestrian flow prediction in extensive road networks using biased observational data
Fußgänger-Verkehrsflussvorhersage in extensiven Straßennetzen mittels verfälschten Beobachtungsdaten
2008
4 Seiten, 2 Bilder, 14 Quellen
Aufsatz (Konferenz)
Englisch
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