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GIS
2008
ACM

Pedestrian flow prediction in extensive road networks using biased observational data

14 years 6 months ago
Pedestrian flow prediction in extensive road networks using biased observational data
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-knnapriori, an efficient nearest neighbor based spatial mining algorithm that allows prior knowledge and deductive models to be included in a straightforward and easy way. Categories and Subject Descriptors I.2.6 [Learning]: Induction, Knowledge acquisition; I.5.1 [Models]; I.5.2 [Design Methodology] General Terms Algorithms, Economics, Reliability, Human Factors, Verification Keywords Spatial data mining, pedestrian flow prediction, sample selection bias, prior knowledge, extensive road networks, large scale data
Michael May, Simon Scheider, Roberto Rösler,
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2008
Where GIS
Authors Michael May, Simon Scheider, Roberto Rösler, Daniel Schulz, Dirk Hecker
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