Prof. Dr.

Wrobel, Stefan

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Now showing 1 - 4 of 4
  • Publication
    Pedestrian quantity estimation with trajectory patterns
    ( 2012)
    Liebig, Thomas
    ;
    Xu, Zhao
    ;
    May, Michael
    ;
    Wrobel, Stefan
    In street-based mobility mining, traffic volume estimation receives increasing attention as it provides important applications such as emergency support systems, quality-of-service evaluation and billboard placement. In many real world scenarios, empirical measurements are usually sparse due to some constraints. On the other hand, pedestrians generally show some movement preferences, especially in closed environments, e.g., train stations. We propose a Gaussian process regression based method for traffic volume estimation, which incorporates topological information and prior knowledge on preferred trajectories with a trajectory pattern kernel. Our approach also enables effectively finding most informative sensor placements. We evaluate our method with synthetic German train station pedestr ian data and real-world episodic movement data from the zoo of Duisburg. The empirical analysis demonstrates that incorporating trajectory patterns can largely improve the traffic prediction accuracy, especially when traffic networks are sparsely monitored.
  • Publication
    Spatiotemporal modeling and analysis. Introduction and overview
    ( 2012)
    Körner, Christine
    ;
    May, Michael
    ;
    Wrobel, Stefan
    Over the past ve to seven years the analysis of trajectory data has established itself as an independent research discipline within the area of data mining. In this article we provide an overview on data characteristics, state-of-the-art preprocessing and analysis methods of trajectory data. We conclude the article with a collection of challenges that arise due to the increasing variety of spatiotemporal data sources and which have to be solved for the application of spatiotemporal analysis methods in practice.
  • Publication
    Spatial data mining in practice
    ( 2010)
    Körner, Christine
    ;
    Hecker, Dirk
    ;
    Krause-Traudes, Maike
    ;
    May, Michael
    ;
    Scheider, Simon
    ;
    Schulz, Daniel
    ;
    Stange, Hendrik
    ;
    Wrobel, Stefan
    Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.