Pedestrian flow prediction in extensive road networks using biased observational data
Michael E May, Simon Scheider, Roberto Rösler, Daniel A. Schulz, Dirk Hecker · 2008
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.