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.


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    Titel :

    Pedestrian flow prediction in extensive road networks using biased observational data


    Weitere Titelangaben:

    Fußgänger-Verkehrsflussvorhersage in extensiven Straßennetzen mittels verfälschten Beobachtungsdaten


    Beteiligte:
    May, Michael (Autor:in) / Scheider, Simon (Autor:in) / Rösler, Roberto (Autor:in) / Schulz, Daniel (Autor:in) / Hecker, Dirk (Autor:in)


    Erscheinungsdatum :

    2008


    Format / Umfang :

    4 Seiten, 2 Bilder, 14 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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