Knowledge-Enriched Route Computation

  • Georgios SkoumasEmail author
  • Klaus Arthur Schmid
  • Gregor Jossé
  • Matthias Schubert
  • Mario A. Nascimento
  • Andreas Züfle
  • Matthias Renz
  • Dieter Pfoser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest or the fastest path within an underlying road network. With the aid of Volunteered Geographic Information (VGI), i.e., geo-spatial information contained in user generated content, we aim at obtaining paths that do not only minimize distance but also lead through more popular areas. Based on the importance of landmarks in Geographic Information Science and in human cognition, we extract a certain kind of VGI, namely spatial relations that define closeness (nearby, next to) between pairs of points of interest (POIs), and quantify them following a probabilistic framework. Subsequently, using Bayesian inference we obtain a crowd-based closeness confidence score between pairs of POIs. We apply this measure to the corresponding road network based on an altered cost function which does not exclusively rely on distance but also takes crowdsourced geo-spatial information into account. Finally, we propose two routing algorithms on the enriched road network. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results – based on real world datasets – show that the paths computed w.r.t. our alternative cost function yield competitive solutions in terms of path length while also providing more “popular” paths, making routing easier and more informative for the user.


Road Network Spatial Relation Target Node Enrichment Ratio Volunteer Geographic Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research leading to these results has received funding from the EU FP7 project GEOSTREAM (grant No. FP7-SME-2012-315631) as well as the Shared-E-Fleet project by the German Federal Ministry of Economics and Technology (grant No. 01ME12107), the Deutsche Forschungsgemeinschaft (DFG) under grant number RE 266/5-1 and from the DAAD supported by the BMBF under grant number 57052426. Mario A. Nascimento has been partially supported by NSERC Canada. Dieter Pfoser has been partially supported by NGA NURI (grant No. HM02101410004).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Georgios Skoumas
    • 1
    Email author
  • Klaus Arthur Schmid
    • 2
  • Gregor Jossé
    • 2
  • Matthias Schubert
    • 2
  • Mario A. Nascimento
    • 3
  • Andreas Züfle
    • 2
  • Matthias Renz
    • 2
  • Dieter Pfoser
    • 4
  1. 1.National Technical University of AthensAthensGreece
  2. 2.Ludwig-Maximilians-Universität MünchenMunichGermany
  3. 3.University of AlbertaEdmontonCanada
  4. 4.George Mason UniversityFairfaxUSA

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